
        import pandas as pd
        
        sti_df = pd.read_csv('STI (Singapore).csv')


        sti_df


        sti_df.columns

Index(['Date', 'Open', 'High', 'Low', 'Close', 'Volume'], dtype='object')


        import talib
        import numpy as np
        
        sti_rsi = talib.RSI(sti_df[' Close'], timeperiod=14)
        
        d = {'Date': sti_df['Date'], ' RSI': sti_rsi}
        sti_rsi_df = pd.DataFrame(data=d)
        sti_rsi_df['RSI Condition'] = sti_rsi_df[' RSI'].copy()
        
        sti_rsi_df[' Close'] = sti_df[' Close']
        sti_rsi_df.loc[sti_rsi_df['RSI Condition'] <= 30, 'RSI Condition'] = 1
        sti_rsi_df.loc[(sti_rsi_df['RSI Condition'] > 30) & (sti_rsi_df['RSI Condition'] < 70), 'RSI Condition'] = 0
        sti_rsi_df.loc[sti_rsi_df['RSI Condition'] >= 70, 'RSI Condition'] = -1
        sti_rsi_df[' RSI'] = sti_rsi_df[' RSI']*30


        sti_rsi_df.describe()


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                            1:'green',
                          -1: 'red'})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        p0 = sns.lineplot(data=sti_df, x="Date", y="Close", ax=ax)
        p1 = sns.lineplot(data=sti_rsi_df, x="Date", y=" RSI", label='RSI Condition', ax=ax)
        p3 = sns.scatterplot(data=sti_rsi_df, x= "Date", y=' Close', hue ='RSI Condition', palette=color_dict, style='RSI Condition', markers=markers, size='RSI Condition', sizes=size_dict, ax=ax)
        p5 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA200", ax=ax)


        sti_macd, sti_macdsignal, sti_macdhist = talib.MACD(sti_df[' Close'], fastperiod=12, slowperiod=26, signalperiod=9)
        
        d = {'Date': sti_df['Date'], ' MACD': sti_macd, ' MACD_SIG': sti_macdsignal}
        sti_macd_df = pd.DataFrame(data=d)
        sti_macd_df['MACD Condition'] = 0
        
        for df_entry in range(len(sti_macd_df)-1):
            #MACD crossing MACD_SIGNAL from above
            if sti_macd_df[' MACD'][df_entry] > sti_macd_df[' MACD_SIG'][df_entry] and sti_macd_df[' MACD'][df_entry+1] < sti_macd_df[' MACD_SIG'][df_entry+1]:
                sti_macd_df['MACD Condition'][df_entry] = -1
                #print(sti_macd_df[' MACD'][df_entry],sti_macd_df[' MACD_SIG'][df_entry],sti_macd_df[' MACD'][df_entry+1],sti_macd_df[' MACD_SIG'][df_entry+1])
            #MACD crossing MACD_SIGNAL from below
            if sti_macd_df[' MACD'][df_entry] < sti_macd_df[' MACD_SIG'][df_entry] and sti_macd_df[' MACD'][df_entry+1] > sti_macd_df[' MACD_SIG'][df_entry+1]:
                sti_macd_df['MACD Condition'][df_entry] = 1
                #print(sti_macd_df[' MACD'][df_entry],sti_macd_df[' MACD_SIG'][df_entry],sti_macd_df[' MACD'][df_entry+1],sti_macd_df[' MACD_SIG'][df_entry+1])
                
        sti_macd_orders_df = sti_macd_df.loc[sti_macd_df['MACD Condition'] != 0]
        sti_macd_orders_df[' Close'] = sti_df[' Close']
        sti_macd_orders_df


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({1:'green',
                          -1: 'red'})
        
        markers = dict({1:'^',
                        -1: 'v'})
        
        p0 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        sti_macd_df[" MACD"] = sti_macd_df[" MACD"]*30
        sti_macd_df[" MACD_SIG"] = sti_macd_df[" MACD_SIG"] *30
        p1 = sns.lineplot(data=sti_macd_df, x="Date", y=" MACD", label='MACD', ax=ax)
        p2 = sns.lineplot(data=sti_macd_df, x="Date", y=" MACD_SIG", label='MACD_SIGNAL', ax=ax)
        p3 = sns.scatterplot(data=sti_macd_orders_df, x= "Date", y=' Close', s=100, hue ='MACD Condition', palette=color_dict, style='MACD Condition', markers=markers, ax=ax)


        sti_ema5 = talib.EMA(sti_df['Close'], timeperiod=5)
        sti_ema25 = talib.EMA(sti_df['Close'], timeperiod=25)
        sti_ema50 = talib.EMA(sti_df['Close'], timeperiod=50)
        sti_ema100 = talib.EMA(sti_df['Close'], timeperiod=100)
        sti_ema200 = talib.EMA(sti_df['Close'], timeperiod=200)
        
        #used with the EMA25 to prevent trend trades too far from the EMA - likely trend reversal back to the EMA
        ema25std = sti_ema_df[' EMA25'].std()*0.33
        
        d = {'Date': sti_df['Date'],' EMA5': sti_ema5, ' EMA25': sti_ema25, ' EMA50': sti_ema50,' EMA100': sti_ema100, ' EMA200': sti_ema200, }
        sti_ema_df = pd.DataFrame(data=d)
        sti_ema_df[' Close'] = sti_df['Close']
        sti_ema_df['EMA Condition'] = 0
        
        sti_ema_df.loc[(sti_ema_df[' Close'] > sti_ema_df[' EMA5']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA25']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA50']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA200']), 'EMA Condition'] = 1
        sti_ema_df.loc[(sti_ema_df[' Close'] < sti_ema_df[' EMA5']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA25']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA50']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA200']), 'EMA Condition'] = -1
        
        sti_ema_df['Trend'] = 0
        
        sti_ema_df.loc[sti_ema_df[' EMA200'] < sti_ema_df['Close'], 'Trend'] = 1
        sti_ema_df.loc[sti_ema_df[' EMA200'] > sti_ema_df['Close'], 'Trend'] = -1
        
        sti_ema_df.loc[sti_ema_df['EMA Condition']!=0]


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                          1:'green',
                          -1: 'red'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        p0 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        p1 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA5", ax=ax)
        p2 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA25", ax=ax)
        p3 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA50", ax=ax)
        p4 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA100", ax=ax)
        p5 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA200", ax=ax)
        
        p6 = sns.scatterplot(data=sti_ema_df, x= "Date", y=' Close', hue='EMA Condition', size='EMA Condition', palette=color_dict, sizes=size_dict, style='EMA Condition', markers=markers, ax=ax)


        sti_ema5 = talib.EMA(sti_df[' Close'], timeperiod=5)
        sti_ema25 = talib.EMA(sti_df[' Close'], timeperiod=25)
        sti_ema50 = talib.EMA(sti_df[' Close'], timeperiod=50)
        sti_ema100 = talib.EMA(sti_df[' Close'], timeperiod=100)
        sti_ema200 = talib.EMA(sti_df[' Close'], timeperiod=200)
        
        #used with the EMA25 to prevent trend trades too far from the EMA - likely trend reversal back to the EMA
        ema25std = sti_ema_df[' EMA25'].std()*0.33
        
        d = {'Date': sti_df['Date'],' EMA5': sti_ema5, ' EMA25': sti_ema25, ' EMA50': sti_ema50,' EMA100': sti_ema100, ' EMA200': sti_ema200, }
        sti_ema_df = pd.DataFrame(data=d)
        sti_ema_df[' Close'] = sti_df[' Close']
        sti_ema_df['EMA Condition'] = 0
        
        sti_ema_df.loc[(sti_ema_df[' Close'] > sti_ema_df[' EMA25']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA50']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA200']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA25']+ema25std), 'EMA Condition'] = 1
        sti_ema_df.loc[(sti_ema_df[' Close'] < sti_ema_df[' EMA25']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA50']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA200']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA25']-ema25std), 'EMA Condition'] = -1
        
        
        sti_ema_df.loc[sti_ema_df['EMA Condition']!=0]


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                          1:'green',
                          -1: 'red'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        p1 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        p2 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA25", ax=ax)
        p3 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA50", ax=ax)
        p4 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA100", ax=ax)
        p5 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA200", ax=ax)
        
        p6 = sns.scatterplot(data=sti_ema_df, x= "Date", y=' Close', hue='EMA Condition', size='EMA Condition', palette=color_dict, sizes=size_dict, style='EMA Condition', markers=markers, ax=ax)


        sti_ema25 = talib.EMA(sti_df[' Close'], timeperiod=25)
        sti_ema50 = talib.EMA(sti_df[' Close'], timeperiod=50)
        sti_ema100 = talib.EMA(sti_df[' Close'], timeperiod=100)
        sti_ema200 = talib.EMA(sti_df[' Close'], timeperiod=200)
        
        #used with the EMA25 to prevent trend trades too far from the EMA - likely trend reversal back to the EMA
        ema25std = sti_ema_df[' EMA25'].std()*0.33
        
        d = {'Date': sti_df['Date'], ' EMA25': sti_ema25, ' EMA50': sti_ema50,' EMA100': sti_ema100, ' EMA200': sti_ema200, }
        sti_ema_df = pd.DataFrame(data=d)
        sti_ema_df[' Close'] = sti_df[' Close']
        sti_ema_df['EMA Condition'] = 0
        
        sti_ema_df.loc[(sti_ema_df[' Close'] < sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] > sti_ema_df[' EMA200']), 'EMA Condition'] = 1
        sti_ema_df.loc[(sti_ema_df[' Close'] > sti_ema_df[' EMA100']) & (sti_ema_df[' Close'] < sti_ema_df[' EMA200']), 'EMA Condition'] = -1
        
        
        sti_ema_df.loc[sti_ema_df['EMA Condition']!=0]


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                          1:'green',
                          -1: 'red'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        p1 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        p2 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA25", ax=ax)
        p3 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA50", ax=ax)
        p4 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA100", ax=ax)
        p5 = sns.lineplot(data=sti_ema_df, x="Date", y=" EMA200", ax=ax)
        
        p6 = sns.scatterplot(data=sti_ema_df, x= "Date", y=' Close', hue='EMA Condition', size='EMA Condition', palette=color_dict, sizes=size_dict, style='EMA Condition', markers=markers, ax=ax)


        sti_atr = talib.ATR(sti_df[' High'], sti_df[' Low'], sti_df[' Close'], timeperiod=14)
        
        d = {'Date': sti_df['Date'], ' ATR': sti_atr }
        sti_atr_df = pd.DataFrame(data=d)
        sti_atr_df[' Close'] = sti_df[' Close']


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        p1 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        p2 = sns.lineplot(data=sti_atr_df, x="Date", y=" ATR", ax=ax)
        
        p6 = sns.scatterplot(data=sti_atr_df, x= "Date", y=' Close', hue=' ATR', size=' ATR', ax=ax)


        sti_bb_upperband, sti_bb_middleband, sti_bb_lowerband = talib.BBANDS(sti_df[' Close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
        
        d = {'Date': sti_df['Date'], ' Upper BB': sti_bb_upperband,' Middle BB': sti_bb_middleband, ' Lower BB': sti_bb_lowerband,}
        sti_bb_df = pd.DataFrame(data=d)
        sti_bb_df[' Close'] = sti_df[' Close']
        sti_bb_df['BB Condition'] = 0
        
        sti_bb_df.loc[(sti_bb_df[' Close'] > sti_bb_df[' Upper BB']), 'BB Condition'] = -1
        sti_bb_df.loc[(sti_bb_df[' Close'] < sti_bb_df[' Lower BB']), 'BB Condition'] = 1
        
        sti_bb_df.loc[sti_bb_df['BB Condition'] != 0]


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                          1:'green',
                          -1: 'red'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        p1 = sns.lineplot(data=sti_df, x="Date", y=" Close", ax=ax)
        p2 = sns.lineplot(data=sti_bb_df, x="Date", y=" Upper BB", ax=ax)
        p3 = sns.lineplot(data=sti_bb_df, x="Date", y=" Lower BB", ax=ax)
        
        p6 = sns.scatterplot(data=sti_bb_df, x= "Date", y=' Close', hue='BB Condition', size='BB Condition', palette=color_dict, sizes=size_dict, style='BB Condition', markers=markers, ax=ax)


        regression_data


        regression_data.describe()


        import numpy as np
        
        future_price_dict = dict()
        
        for future in [1,3,5,10,15,30]:
            future_price_dict['future_price'+str(future)] = list(regression_data[' Close'])[future:] + [np.NaN for i in range(future)]
            
        future_price_dict['future_price3']

        [3068.12,
         3089.65,
         3051.11,
         3086.7,
         ...]


        for future_price in future_price_dict:
            regression_data[future_price] = future_price_dict[future_price]


        regression_data.loc[regression_data['MACD Condition']==-1].describe()


        regression_data.loc[(regression_data['MACD Condition']==-1) & (regression_data['Trend']==-1)].describe()


        regression_data.loc[(regression_data['MACD Condition']==1)].describe()


        regression_data.loc[(regression_data['MACD Condition']==1) & (regression_data['Trend']==1)].describe()


        regression_data.loc[(regression_data['RSI Condition']==1) & (regression_data['Trend']==1)].describe()


        regression_data.loc[(regression_data['RSI Condition']==1)].describe()


        regression_data.loc[(regression_data['RSI Condition']==-1)].describe()


        regression_data.loc[(regression_data['RSI Condition']==-1) & (regression_data['Trend']==-1)].describe()


        regression_data.loc[(regression_data['BB Condition']==1)].describe()


        regression_data.loc[(regression_data['BB Condition']==1) & (regression_data['Trend']==1)].describe()


        regression_data.loc[(regression_data['BB Condition']==-1)].describe()


        regression_data.loc[(regression_data['BB Condition']==-1) & (regression_data['Trend']==-1)].describe()


        import pandas as pd
        
        btc_df = pd.read_csv('BTC-USD.csv')
        bnb_df = pd.read_csv('BNB-USD.csv')
        doge_df = pd.read_csv('DOGE-USD.csv')
        sol_df = pd.read_csv('SOL1-USD.csv')
        ada_df = pd.read_csv('ADA-USD.csv')
        xrp_df = pd.read_csv('XRP-USD.csv')
        eth_df = pd.read_csv('ETH-USD.csv')
        
        oil_df = pd.read_excel('Oil.xlsx')
        gold_df = pd.read_csv('Gold.csv')
        sp500_df = pd.read_csv('SP500.csv')
        sti_df = pd.read_csv('STI.csv')


        btc_hourly_df = btc_df = pd.read_csv('reddit_bitcoin_1h.csv')


        oil_df
        oil_df = oil_df[pd.to_numeric(oil_df['Close'], errors='coerce').notnull()] 
        oil_df = oil_df[pd.to_numeric(oil_df['Low'], errors='coerce').notnull()] 
        oil_df = oil_df[pd.to_numeric(oil_df['High'], errors='coerce').notnull()]
        oil_df = oil_df[pd.to_numeric(oil_df['Volume'], errors='coerce').notnull()]
        
        for column in oil_df.columns:
            try:
                oil_df[column] = pd.to_numeric(oil_df[column])
            except:
                pass
            
        oil_df.reset_index(inplace=True)


        for column in gold_df.columns:
            gold_df[column] = gold_df[column].str.replace(r',', '')
            
        gold_df = gold_df[pd.to_numeric(gold_df['Close'], errors='coerce').notnull()] 
        gold_df = gold_df[pd.to_numeric(gold_df['Low'], errors='coerce').notnull()] 
        gold_df = gold_df[pd.to_numeric(gold_df['High'], errors='coerce').notnull()]
        gold_df = gold_df[pd.to_numeric(gold_df['Volume'], errors='coerce').notnull()]
        
        for column in gold_df.columns:
            try:
                gold_df[column] = pd.to_numeric(gold_df[column])
            except:
                pass
            
        gold_df.reset_index(inplace=True)


        for column in sp500_df.columns:
            sp500_df[column] = sp500_df[column].str.replace(r',', '')
            
        sp500_df = sp500_df[pd.to_numeric(sp500_df['Close'], errors='coerce').notnull()] 
        sp500_df = sp500_df[pd.to_numeric(sp500_df['Low'], errors='coerce').notnull()] 
        sp500_df = sp500_df[pd.to_numeric(sp500_df['High'], errors='coerce').notnull()]
        sp500_df = sp500_df[pd.to_numeric(sp500_df['Volume'], errors='coerce').notnull()]
        
        for column in sp500_df.columns:
            try:
                sp500_df[column] = pd.to_numeric(sp500_df[column])
            except:
                pass
        sp500_df.reset_index(inplace=True)


        sti_df


        #input df containing 'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'\
        #output TA regression data
        
        import talib
        import numpy as np
        
        def generate_regression_data(df):
        
        
            rsi = talib.RSI(df['Close'], timeperiod=14)
        
            d = {'Date': df['Date'], ' RSI': rsi}
            rsi_df = pd.DataFrame(data=d)
            rsi_df['RSI Condition'] = rsi_df[' RSI'].copy()
        
            rsi_df.loc[rsi_df['RSI Condition'] <= 30, 'RSI Condition'] = 1
            rsi_df.loc[(rsi_df['RSI Condition'] > 30) & (rsi_df['RSI Condition'] < 70), 'RSI Condition'] = 0
            rsi_df.loc[rsi_df['RSI Condition'] >= 70, 'RSI Condition'] = -1 
            
            macd, macdsignal, macdhist = talib.MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)
        
            d = {'Date': df['Date'], ' MACD': macd, ' MACD_SIG': macdsignal}
            macd_df = pd.DataFrame(data=d)
            macd_df['MACD Condition'] = 0
        
            for df_entry in range(len(macd_df)-1):
                #MACD crossing MACD_SIGNAL from above
                if macd_df[' MACD'][df_entry] > macd_df[' MACD_SIG'][df_entry] and macd_df[' MACD'][df_entry+1] < macd_df[' MACD_SIG'][df_entry+1]:
                    macd_df['MACD Condition'][df_entry] = -1
                    #print(macd_df[' MACD'][df_entry],macd_df[' MACD_SIG'][df_entry],macd_df[' MACD'][df_entry+1],macd_df[' MACD_SIG'][df_entry+1])
                #MACD crossing MACD_SIGNAL from below
                if macd_df[' MACD'][df_entry] < macd_df[' MACD_SIG'][df_entry] and macd_df[' MACD'][df_entry+1] > macd_df[' MACD_SIG'][df_entry+1]:
                    macd_df['MACD Condition'][df_entry] = 1
                    #print(macd_df[' MACD'][df_entry],macd_df[' MACD_SIG'][df_entry],macd_df[' MACD'][df_entry+1],macd_df[' MACD_SIG'][df_entry+1])
        
            ema5 = talib.EMA(df['Close'], timeperiod=5)
            ema25 = talib.EMA(df['Close'], timeperiod=25)
            ema50 = talib.EMA(df['Close'], timeperiod=50)
            ema100 = talib.EMA(df['Close'], timeperiod=100)
            ema200 = talib.EMA(df['Close'], timeperiod=200)
        
            d = {'Date': df['Date'],' EMA5': ema5, ' EMA25': ema25, ' EMA50': ema50,' EMA100': ema100, ' EMA200': ema200, }
            
            ema_df = pd.DataFrame(data=d)
            ema_df['Close'] = df['Close']
            ema_df['EMA Condition'] = 0
            
            #used with the EMA25 to prevent trend trades too far from the EMA - likely trend reversal back to the EMA
            ema25std = ema_df[' EMA25'].std()*0.33
            
            ema_df.loc[(ema_df['Close'] > ema_df[' EMA5']) & (ema_df['Close'] > ema_df[' EMA25']) & (ema_df['Close'] > ema_df[' EMA50']) & (ema_df['Close'] > ema_df[' EMA100']) & (ema_df['Close'] > ema_df[' EMA200']), 'EMA Condition'] = 1
            ema_df.loc[(ema_df['Close'] < ema_df[' EMA5']) & (ema_df['Close'] < ema_df[' EMA25']) & (ema_df['Close'] < ema_df[' EMA50']) & (ema_df['Close'] < ema_df[' EMA100']) & (ema_df['Close'] < ema_df[' EMA200']), 'EMA Condition'] = -1
        
            ema_df['Trend'] = 0
        
            ema_df.loc[ema_df[' EMA200'] < ema_df['Close'], 'Trend'] = 1
            ema_df.loc[ema_df[' EMA200'] > ema_df['Close'], 'Trend'] = -1 
            
            ema_df['Close-EMA200 Price Diff'] = ema_df['Close'] - ema_df[' EMA200']
            
            atr = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
        
            d = {'Date': df['Date'], ' ATR': atr }
            atr_df = pd.DataFrame(data=d)
            atr_df['Close'] = df['Close']
            
            bb_upperband, bb_middleband, bb_lowerband = talib.BBANDS(df['Close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
        
            d = {'Date': df['Date'], ' Upper BB': bb_upperband,' Middle BB': bb_middleband, ' Lower BB': bb_lowerband,}
            bb_df = pd.DataFrame(data=d)
            bb_df['Close'] = df['Close']
            bb_df['BB Condition'] = 0
        
            bb_df.loc[(bb_df['Close'] > bb_df[' Upper BB']), 'BB Condition'] = -1
            bb_df.loc[(bb_df['Close'] < bb_df[' Lower BB']), 'BB Condition'] = 1
        
            
            # Windowed regression based regressors
            forecasted = talib.TSF(df['Close'], timeperiod=14)
        
            d = {'Date': df['Date'], 'Close': df['Close'],'Forecasted': forecasted}
            forecasted_df = pd.DataFrame(data=d)
        
        
            slope = talib.LINEARREG_SLOPE(df['Close'], timeperiod=14)
        
            d = {'Date': df['Date'], 'Close': df['Close'], 'Slope': slope}
            slope_df = pd.DataFrame(data=d)
        
            acceleration = talib.LINEARREG_SLOPE(slope_df['Slope'], timeperiod=14)
        
            d = {'Date': df['Date'], 'Close': df['Close'],'Acceleration': acceleration}
            acceleration_df = pd.DataFrame(data=d)
            
            # Merge all datafraems
        
            rsi_cols_to_use = rsi_df.columns.difference(bb_df.columns)
            ema_cols_to_use = ema_df.columns.difference(bb_df.columns)
            atr_cols_to_use = atr_df.columns.difference(bb_df.columns)
            macd_cols_to_use = macd_df.columns.difference(bb_df.columns)
            forecasted_cols_to_use = forecasted_df.columns.difference(bb_df.columns)
            slope_cols_to_use = slope_df.columns.difference(bb_df.columns)
            acceleration_cols_to_use = acceleration_df.columns.difference(bb_df.columns)
        
            result = pd.concat([rsi_df[rsi_cols_to_use],macd_df[macd_cols_to_use],ema_df[ema_cols_to_use],
                                atr_df[atr_cols_to_use],forecasted_df[forecasted_cols_to_use], bb_df,
                               slope_df[slope_cols_to_use], acceleration_df[acceleration_cols_to_use]],axis=1)
            
            result['Volume'] = df['Volume']
            
            # Volume is partially related to price, and effect can be slightly corrected by making it relative to price
            result['Price Relative Volume'] = result['Volume']/result['Close']
        
            # Volatility should be relative to price
            result['Price Relative ATR'] = result[' ATR']/result['Close']*100
            result['Price Relative Close-EMA200 Price Diff'] = result['Close-EMA200 Price Diff']/result['Close']
            result['Forecasted % Profit'] = (result['Forecasted'] - result['Close'])/result['Close']
        
        
            # Price corrected volume can then be logged to control for decreased marginal effects
            result['Log Price Relative Volume'] = np.log(result['Price Relative Volume'])
        
            # Price corrected ATR can then be logged to control for decreased marginal effects
            result['Log Price Relative ATR'] = np.log(result['Price Relative ATR'])
        
            result['Price Diff Sign'] = 0
            result.loc[result['Price Relative Close-EMA200 Price Diff']<0,'Price Diff Sign'] = -1
            result.loc[result['Price Relative Close-EMA200 Price Diff']>0,'Price Diff Sign'] = 1
        
            result['Log(Price Relative Close-EMA200 Price Diff)'] = np.log(np.sqrt((result['Price Relative Close-EMA200 Price Diff'])**2+1))*result['Price Diff Sign']
            result['Log(Price Relative Volume)'] = np.log(result['Price Relative Volume'])
            result['Log(Price Relative ATR)'] = np.log(result['Price Relative ATR'])
        
            result['RSI x Trend'] = result[' RSI'] * result['Trend']
            result['RSI x Price Relative Close-EMA200 Price Diff'] = result[' RSI'] * result['Price Relative Close-EMA200 Price Diff']
            result['RSI Condition x Trend'] = result['RSI Condition'] * result['Trend']
            result['RSI Condition x Price Relative Close-EMA200 Price Diff'] = result['RSI Condition'] * result['Price Relative Close-EMA200 Price Diff']
        
            result['MACD Condition x Trend'] = result['MACD Condition'] * result['Trend']
            result['MACD Condition x Price Relative Close-EMA200 Price Diff'] = result['MACD Condition'] * result['Price Relative Close-EMA200 Price Diff']
        
            regression_data = result[['Date','Close', 'Volume',' RSI', 'RSI Condition', 'MACD Condition', 
                                      'EMA Condition','Close-EMA200 Price Diff', 'BB Condition', ' ATR', 'Trend',
                                     'Forecasted', 'Slope', 'Acceleration','Price Relative Close-EMA200 Price Diff','Price Relative ATR',
                                      
                                     ]]
        
        
            future_price_dict = dict()
        
        
            for future in [1,3,5,10,15,30]:
                future_price_dict['future_price'+str(future)] = list(regression_data['Close'])[future:] + [np.NaN for i in range(future)]
                
            for future_price in future_price_dict:
                regression_data[future_price] = future_price_dict[future_price]
                
            for future_price in future_price_dict:
                regression_data[future_price + ' P/L'] = regression_data[future_price] - regression_data['Close']
                regression_data[future_price + ' % P/L'] = (regression_data[future_price] - regression_data['Close'])/regression_data['Close']
            regression_data.dropna(inplace= True)
            
        
            return(regression_data)


        btc_regression_data = generate_regression_data(btc_df)
        oil_regression_data = generate_regression_data(oil_df)
        gold_regression_data = generate_regression_data(gold_df)
        sp500_regression_data = generate_regression_data(sp500_df)
        sti_regression_data = generate_regression_data(sti_df)


        btc_hourly_df


        btc_hourly_regression = generate_regression_data(btc_hourly_df)


        bnb_df_regression_data = generate_regression_data(bnb_df)
        doge_df_regression_data = generate_regression_data(doge_df)
        sol_df_regression_data = generate_regression_data(sol_df)
        ada_df_regression_data = generate_regression_data(ada_df)
        xrp_df_regression_data = generate_regression_data(xrp_df)
        eth_df_regression_data = generate_regression_data(eth_df)


        btc_regression_data.to_csv('BTC Regression Data.csv')
        oil_regression_data.to_csv('Oil Regression Data.csv')
        gold_regression_data.to_csv('Gold Regression Data.csv')
        sp500_regression_data.to_csv('SP500 Regression Data.csv')
        sti_regression_data.to_csv('STI Regression Data.csv')


        bnb_df_regression_data.to_csv('BNB Regression Data.csv')
        doge_df_regression_data.to_csv('DOGE Regression Data.csv')
        sol_df_regression_data.to_csv('SOL Regression Data.csv')
        ada_df_regression_data.to_csv('ADA Regression Data.csv')
        xrp_df_regression_data.to_csv('XRP Regression Data.csv')
        eth_df_regression_data.to_csv('ETH Regression Data.csv')


        btc_regression_data.loc[(btc_regression_data['MACD Condition']==1) & (btc_regression_data['Trend']==1)].describe()


        btc_regression_data.loc[(btc_regression_data['MACD Condition']==-1) & (btc_regression_data['Trend']==-1)].describe()


        btc_regression_data.loc[(btc_regression_data['RSI Condition']==1) & (btc_regression_data['Trend']==1)].describe()


        btc_regression_data.loc[(btc_regression_data['RSI Condition']==-1) & (btc_regression_data['Trend']==-1)].describe()


        btc_regression_data.loc[(btc_regression_data['RSI Condition']==1)].describe()


        btc_regression_data.loc[(btc_regression_data['RSI Condition']==-1)].describe()


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                            1:'green',
                          -1: 'red'})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        
        p0 = sns.lineplot(data=btc_regression_data, x="Date", y="Close", ax=ax)
        
        p3 = sns.scatterplot(data=btc_regression_data, x= "Date", y='Close', hue ='RSI Condition', palette=color_dict, style='RSI Condition', markers=markers, size='RSI Condition', sizes=size_dict, ax=ax)


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                            1:'green',
                          -1: 'red'})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        
        p0 = sns.lineplot(data=oil_regression_data, x="Date", y="Close", ax=ax)
        
        p3 = sns.scatterplot(data=oil_regression_data, x= "Date", y='Close', hue ='RSI Condition', palette=color_dict, style='RSI Condition', markers=markers, size='RSI Condition', sizes=size_dict, ax=ax)


        btc_regression_data = generate_regression_data(btc_df)


        btc_forecasted = talib.TSF(btc_regression_data['Close'], timeperiod=14)
        
        d = {'Date': btc_regression_data['Date'], 'Close': btc_regression_data['Close'],'Forecasted': btc_forecasted}
        btc_forecasted_df = pd.DataFrame(data=d)
        
        
        btc_slope = talib.LINEARREG_SLOPE(btc_regression_data['Close'], timeperiod=14)
        
        d = {'Date': btc_regression_data['Date'], 'Close': btc_regression_data['Close'], 'Slope': btc_slope}
        btc_slope_df = pd.DataFrame(data=d)
        
        btc_acceleration = talib.LINEARREG_SLOPE(btc_slope_df['Slope'], timeperiod=14)
        
        d = {'Date': btc_regression_data['Date'], 'Close': btc_regression_data['Close'],'Acceleration': btc_acceleration}
        btc_acceleration_df = pd.DataFrame(data=d)


        btc_forecasted_df


        btc_slope_df


        btc_acceleration_df


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                            1:'green',
                          -1: 'red'})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        
        p0 = sns.lineplot(data=btc_regression_data[:200], x="Date", y="Close", ax=ax)
        p1 = sns.lineplot(data=btc_forecasted_df[:200], x="Date", y="Forecasted", ax=ax)
        
        # p3 = sns.scatterplot(data=btc_regression_data, x= "Date", y='Close', hue ='RSI Condition', palette=color_dict, style='RSI Condition', markers=markers, size='RSI Condition', sizes=size_dict, ax=ax)


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({0:'black',
                            1:'green',
                          -1: 'red'})
        
        markers = dict({0:'o',
                        1:'^',
                        -1: 'v'})
        
        size_dict = dict({0:3,
                          1:100,
                          -1:100})
        
        p2 = sns.lineplot(data=btc_slope_df[:200], x="Date", y="Slope", ax=ax)
        p3 = sns.lineplot(data=btc_acceleration_df[:200], x="Date", y="Acceleration", ax=ax)
        # p3 = sns.scatterplot(data=btc_regression_data, x= "Date", y='Close', hue ='RSI Condition', palette=color_dict, style='RSI Condition', markers=markers, size='RSI Condition', sizes=size_dict, ax=ax)


        import numpy as np
        import pandas as pd
        import seaborn as sns
        import statsmodels
        from statsmodels.tsa.api import ARDL
        from statsmodels.datasets import danish_data
        data = danish_data.load_pandas().data
        lrm = data.lrm
        exog = data[["lry", "ibo", "ide"]]


        # this is the endogenous response variable. The dependent variable.
        lrm

period
        1974-01-01    11.632550
        1974-04-01    11.604152
        1974-07-01    11.581521
        1974-10-01    11.601847
        1975-01-01    11.586305
        1975-04-01    11.604499
        1975-07-01    11.653307
        1975-10-01    11.764246
        1976-01-01    11.752934
        1976-04-01    11.765480
        1976-07-01    11.780583
        1976-10-01    11.770178
        1977-01-01    11.745892
        1977-04-01    11.768651
        1977-07-01    11.749825
        1977-10-01    11.748697
        1978-01-01    11.704916
        1978-04-01    11.703444
        1978-07-01    11.703001
        1978-10-01    11.703673
        1979-01-01    11.678903
        1979-04-01    11.707782
        1979-07-01    11.676155
        1979-10-01    11.685921
        1980-01-01    11.641606
        1980-04-01    11.635180
        1980-07-01    11.609261
        1980-10-01    11.657613
        1981-01-01    11.627818
        1981-04-01    11.628852
        1981-07-01    11.603778
        1981-10-01    11.629065
        1982-01-01    11.602023
        1982-04-01    11.603775
        1982-07-01    11.594622
        1982-10-01    11.603820
        1983-01-01    11.617126
        1983-04-01    11.688096
        1983-07-01    11.726515
        1983-10-01    11.781250
        1984-01-01    11.797620
        1984-04-01    11.841912
        1984-07-01    11.840295
        1984-10-01    11.923578
        1985-01-01    11.902962
        1985-04-01    11.926784
        1985-07-01    11.970998
        1985-10-01    12.025903
        1986-01-01    12.051151
        1986-04-01    12.075460
        1986-07-01    12.056189
        1986-10-01    12.071628
        1987-01-01    12.027952
        1987-04-01    12.039788
        1987-07-01    12.015294
        Freq: QS-JAN, Name: lrm, dtype: float64


        exog


        model1 = ARDL(data.lrm, 3, data[["lry", "ibo", "ide"]], 3)
        results = model1.fit()
        results.summary()


        btc_regression_data.set_index('Date')
        btc_regression_data.columns

Index(['Date', 'Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration',
               'Price Relative Close-EMA200 Price Diff', 'Price Relative ATR',
               'future_price1', 'future_price3', 'future_price5', 'future_price10',
               'future_price15', 'future_price30', 'future_price1 P/L',
               'future_price1 % P/L', 'future_price3 P/L', 'future_price3 % P/L',
               'future_price5 P/L', 'future_price5 % P/L', 'future_price10 P/L',
               'future_price10 % P/L', 'future_price15 P/L', 'future_price15 % P/L',
               'future_price30 P/L', 'future_price30 % P/L'],
              dtype='object')


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 10, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 10)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 8, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 8)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 6, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 6)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 4, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 4)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 2, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 2)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 0, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 0)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict(dynamic=True)-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0015459651576634926


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0014454304066524632


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 5, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 5, causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        len(results.predict())
        len(btc_regression_data['future_price1 % P/L'])

1834


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict(dynamic=True)-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001461337237831895


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001408179001769347


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013463983932337931


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='n')
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013864011633899058


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='t')
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013972638723326021


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='ct')
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001399154178203941


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013463983932337931


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 3, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 3)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 5, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 5)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price3 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price3 % P/L']], 3, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 3)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price3 % P/L']], 5, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 5)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price5 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price5 % P/L']], 3, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 3)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model2 = ARDL(btc_regression_data[['future_price5 % P/L']], 5, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 5)
        results2 = model2.fit()
        results2.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013603198470945046


        btc_regression_data.columns

Index(['Date', 'Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration', 'future_price1',
               'future_price3', 'future_price5', 'future_price10', 'future_price15',
               'future_price30', 'future_price1 P/L', 'future_price1 % P/L',
               'future_price3 P/L', 'future_price3 % P/L', 'future_price5 P/L',
               'future_price5 % P/L', 'future_price10 P/L', 'future_price10 % P/L',
               'future_price15 P/L', 'future_price15 % P/L', 'future_price30 P/L',
               'future_price30 % P/L', 'Log Volume', 'Price Relative Volume',
               'Price Relative ATR', 'Price Relative Close-EMA200 Price Diff',
               'Directed RSI', 'Log Price Relative Volume', 'Log Price Relative ATR',
               'RSI x Trend', 'RSI Condition x Trend', 'RSI x Close-EMA200 Price Diff',
               'RSI Condition x Close-EMA200 Price Diff'],
              dtype='object')


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001399154178203941


        # Volume is partially related to price, and effect can be slightly corrected by making it relative to price
        btc_regression_data['Price Relative Volume'] = btc_regression_data['Volume']/btc_regression_data['Close']
        
        # Volatility should be relative to price
        btc_regression_data['Price Relative ATR'] = btc_regression_data[' ATR']/btc_regression_data['Close']*100
        btc_regression_data['Price Relative Close-EMA200 Price Diff'] = btc_regression_data['Close-EMA200 Price Diff']/btc_regression_data['Close']
        btc_regression_data['Forecasted % Profit'] = (btc_regression_data['Forecasted'] - btc_regression_data['Close'])/btc_regression_data['Close']
        
        
        # Price corrected volume can then be logged to control for decreased marginal effects
        btc_regression_data['Log Price Relative Volume'] = np.log(btc_regression_data['Price Relative Volume'])
        
        # Price corrected ATR can then be logged to control for decreased marginal effects
        btc_regression_data['Log Price Relative ATR'] = np.log(btc_regression_data['Price Relative ATR'])


        btc_regression_data['Forecasted % Profit']

199    -0.027673
        200    -0.031912
        201     0.003774
        202     0.013601
        203     0.037929
                  ...   
        2031    0.019147
        2032    0.082167
        2033    0.073383
        2035    0.045646
        2037    0.039501
        Name: Forecasted % Profit, Length: 1834, dtype: float64


        btc_regression_data['Price Relative ATR']

199     3.920327
        200     3.783270
        201     3.785180
        202     3.648472
        203     3.781457
                  ...   
        2031    5.414259
        2032    5.868836
        2033    5.596508
        2035    5.450755
        2037    5.325498
        Name: Price Relative ATR, Length: 1834, dtype: float64


        btc_regression_data['Log Price Relative ATR']

199     1.366175
        200     1.330589
        201     1.331093
        202     1.294308
        203     1.330109
                  ...   
        2031    1.689036
        2032    1.769656
        2033    1.722143
        2035    1.695754
        2037    1.672506
        Name: Log Price Relative ATR, Length: 1834, dtype: float64


        # Volume, ATR, Close-EMA200 Price Diff, scale change over time but % change expected is the same, they need to be refactored


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001391963862190577


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        btc_regression_data['Price Diff Sign'] = 0
        btc_regression_data.loc[btc_regression_data['Price Relative Close-EMA200 Price Diff']<0,'Price Diff Sign'] = -1
        btc_regression_data.loc[btc_regression_data['Price Relative Close-EMA200 Price Diff']>0,'Price Diff Sign'] = 1
        
        btc_regression_data['Log(Price Relative Close-EMA200 Price Diff)'] = np.log(np.sqrt((btc_regression_data['Price Relative Close-EMA200 Price Diff'])**2+1))*btc_regression_data['Price Diff Sign']
        btc_regression_data['Log(Price Relative Volume)'] = np.log(btc_regression_data['Price Relative Volume'])
        btc_regression_data['Log(Price Relative ATR)'] = np.log(btc_regression_data['Price Relative ATR'])


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Log(Price Relative Volume)', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Log(Price Relative Close-EMA200 Price Diff)', 'BB Condition', 'Log(Price Relative ATR)',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Log(Price Relative Volume)', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0014010187392875112


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume','Log(Price Relative Volume)', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        # Interaction Terms for RSI and MACD
        btc_regression_data['RSI x Trend'] = btc_regression_data[' RSI'] * btc_regression_data['Trend']
        btc_regression_data['RSI x Price Relative Close-EMA200 Price Diff'] = btc_regression_data[' RSI'] * btc_regression_data['Price Relative Close-EMA200 Price Diff']
        btc_regression_data['RSI Condition x Trend'] = btc_regression_data['RSI Condition'] * btc_regression_data['Trend']
        btc_regression_data['RSI Condition x Price Relative Close-EMA200 Price Diff'] = btc_regression_data['RSI Condition'] * btc_regression_data['Price Relative Close-EMA200 Price Diff']
        
        btc_regression_data['MACD Condition x Trend'] = btc_regression_data['MACD Condition'] * btc_regression_data['Trend']
        btc_regression_data['MACD Condition x Price Relative Close-EMA200 Price Diff'] = btc_regression_data['MACD Condition'] * btc_regression_data['Price Relative Close-EMA200 Price Diff']


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume','Log(Price Relative Volume)', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration','RSI x Trend','RSI x Price Relative Close-EMA200 Price Diff', 'RSI Condition x Trend',
                                                                                            'RSI Condition x Price Relative Close-EMA200 Price Diff',
                                                                                            'MACD Condition x Trend',
                                                                                            'MACD Condition x Price Relative Close-EMA200 Price Diff'
                                                                                           
                                                                                           ]], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0014004894923544471


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Log(Price Relative Volume)', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Log(Price Relative Close-EMA200 Price Diff)', 'BB Condition', 'Log(Price Relative ATR)',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted % Profit', 'Slope', 'Acceleration']], 1, trend='n', causal=False)
        results = model1.fit()
        results.summary()


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0014015956857274317


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.001361241404925019


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013612414049252724


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','Log Price Relative Volume']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013615332411234078


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','Log Price Relative Volume','Log Price Relative ATR']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0013613587426583177


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','RSI x Trend']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','RSI Condition x Trend']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','RSI x Close-EMA200 Price Diff']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Close', 'Price Relative Volume', 'Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff', 'BB Condition', 'Price Relative ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration','RSI Condition x Close-EMA200 Price Diff']], 1, trend='ct', causal=True)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        model1 = ARDL(btc_regression_data[['future_price1 % P/L']], 1, btc_regression_data[['Directed RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Price Relative Close-EMA200 Price Diff','Price Relative ATR',
               'Trend','Forecasted % Profit']], 1, trend='ct', causal=False)
        results = model1.fit()
        results.summary()

D:\ANACONDA\lib\site-packages\statsmodels\tsa\base\tsa_model.py:590: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
          warnings.warn('An unsupported index was provided and will be'


        T = len(results.predict())
        p = 1
        
        RMSE = ((T+p+1)/(T-p-1))*((np.nansum((results.predict()-btc_regression_data['future_price1 % P/L'])**2))/T)
        RMSE

0.0014162329635100466


        btc_regression_data.columns

Index(['Date', 'Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration',
               'Price Relative Close-EMA200 Price Diff', 'Price Relative ATR',
               'future_price1', 'future_price3', 'future_price5', 'future_price10',
               'future_price15', 'future_price30', 'future_price1 P/L',
               'future_price1 % P/L', 'future_price3 P/L', 'future_price3 % P/L',
               'future_price5 P/L', 'future_price5 % P/L', 'future_price10 P/L',
               'future_price10 % P/L', 'future_price15 P/L', 'future_price15 % P/L',
               'future_price30 P/L', 'future_price30 % P/L'],
              dtype='object')


        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == 1].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == 1].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == -1].mean()*100
        oil_long_mean = oil_regression_data[200:]['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == 1].mean()*100
        oil_short_mean = -oil_regression_data[200:]['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == -1].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == 1].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == -1].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == 1].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == -1].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == 1].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == -1].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == -1].std()
        oil_long_std = oil_regression_data[200:]['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == 1].std()
        oil_short_std = oil_regression_data[200:]['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == -1].std()


        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})


        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(sum(rsi_basic_profit["Mean % Profit"]/len(rsi_basic_profit["Mean % Profit"])))

0.004502892259617505


        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['RSI Condition'] == 1) & (btc_regression_data['Trend'] == 1)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1 & (btc_regression_data['Trend'] == -1)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == 1)  & (doge_df_regression_data['Trend'] == 1)].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == -1) & (doge_df_regression_data['Trend'] == -1)].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == 1) & (oil_regression_data['Trend'] == 1)].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == -1) & (oil_regression_data['Trend'] == -1)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == 1) & (gold_regression_data['Trend'] == 1)].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == -1) & (gold_regression_data['Trend'] == -1)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == 1) & (sp500_regression_data['Trend'] == 1)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == -1) & (sp500_regression_data['Trend'] == -1)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == 1) & (sti_regression_data['Trend'] == 1)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == -1) & (sti_regression_data['Trend'] == -1)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == -1].std()


        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})


        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

0.15690525179599085


        diff = 0.1
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['RSI Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        oil_long_mean = oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)].mean()*100
        oil_short_mean = -oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)  ].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > diff)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > diff)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['RSI Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('doge long trades:',len(doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 3
        doge long trades: 2
        2.5307981438548532


        diff = 0.1
        volatility = 10
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['RSI Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        oil_long_mean = oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >diff) & (oil_regression_data['Price Relative ATR'] < volatility)].mean()*100
        oil_short_mean = -oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (oil_regression_data['Price Relative ATR'] < volatility)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)  & (gold_regression_data['Price Relative ATR'] < volatility)].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['RSI Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (gold_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (sp500_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['RSI Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (sp500_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (sti_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['RSI Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (sti_regression_data['Price Relative ATR'] < volatility)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['RSI Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['RSI Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['RSI Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['RSI Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['RSI Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['RSI Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['RSI Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('doge long trades:',len(doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['RSI Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 3
        doge long trades: 2
        4.492782652002879


        oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['RSI Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'])]

199     0.003041
        200    -0.009475
        201     0.016453
        212    -0.013826
        213    -0.046265
                  ...   
        1019   -0.005849
        1020    0.030432
        1039   -0.012274
        1040    0.033909
        1046    0.010444
        Name: future_price1 % P/L, Length: 86, dtype: float64


        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

1.776548834429258


        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Trend'] == 1)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Trend'] == -1)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Trend'] == 1)].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Trend'] == -1)].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Trend'] == 1)].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Trend'] == -1)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Trend'] == 1)].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Trend'] == -1)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Trend'] == 1)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Trend'] == -1)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Trend'] == 1)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Trend'] == -1)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

2.013012634113972


        # THis enhanced strategy assumes trend only holds when certain distance from EMA, afterwhich its actually deviating too much from EMA and expecting a reversal


        diff = 0.8
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

1.8902093463025842


        diff = 0.1
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)  ].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > diff)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) ].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > diff)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('doge long trades:',len(doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 25
        doge long trades: 7
        2.7405125062948277


        diff = 0.5
        volatility = 30
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        oil_long_mean = oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >diff) & (oil_regression_data['Price Relative ATR'] < volatility)].mean()*100
        oil_short_mean = -oil_regression_data[200:]['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (oil_regression_data['Price Relative ATR'] < volatility)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)  & (gold_regression_data['Price Relative ATR'] < volatility)].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (gold_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (sp500_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (sp500_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (sti_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (sti_regression_data['Price Relative ATR'] < volatility)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('doge long trades:',len(doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 1
        doge long trades: 2
        6.709323200083046


        btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]

436    -0.001846
        885    -0.006212
        988     0.033503
        1036    0.239361
        1079    0.040103
        1104   -0.008589
        1139    0.055113
        1156   -0.020669
        1206    0.005594
        1229   -0.041565
        1711    0.077078
        1731    0.016626
        1779    0.011395
        Name: future_price1 % P/L, dtype: float64


        import matplotlib.pyplot as plt
        import numpy as np
        import seaborn as sns
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        color_dict = dict({1:'green',
                          -1: 'red'})
        
        markers = dict({1:'^',
                        -1: 'v'})
        
        p0 = sns.lineplot(data=doge_df_regression_data, x="Date", y="Close", ax=ax)
        # sti_macd_df[" MACD"] = sti_macd_df[" MACD"]*30
        # sti_macd_df[" MACD_SIG"] = sti_macd_df[" MACD_SIG"] *30
        # p1 = sns.lineplot(data=sti_macd_df, x="Date", y=" MACD", label='MACD', ax=ax)
        # p2 = sns.lineplot(data=sti_macd_df, x="Date", y=" MACD_SIG", label='MACD_SIGNAL', ax=ax)
        # p3 = sns.scatterplot(data=sti_macd_orders_df, x= "Date", y=' Close', s=100, hue ='MACD Condition', palette=color_dict, style='MACD Condition', markers=markers, ax=ax)


        btc_hourly_regression


        diff = 0.1
        volatility = 30
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        btc_hourly_long_mean = btc_hourly_regression['future_price1 % P/L'].loc[(btc_hourly_regression['MACD Condition'] == 1) & (btc_hourly_regression['Price Relative Close-EMA200 Price Diff'] > diff) & (btc_hourly_regression['Price Relative ATR'] < volatility)].mean()*100
        btc_hourly_short_mean = -btc_hourly_regression['future_price1 % P/L'].loc[btc_hourly_regression['MACD Condition'] == -1 & (btc_hourly_regression['Price Relative Close-EMA200 Price Diff'] < -diff) & (btc_hourly_regression['Price Relative ATR'] < volatility)].mean()*100
        
        d = {'Index':['BTC Long', 'BTC Short', 'BTC Hourly Long', 'BTC Hourly Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,btc_hourly_long_mean,btc_hourly_short_mean]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('btc short trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('btc hourly long trades:',len(btc_hourly_regression['future_price1 % P/L'].loc[(btc_hourly_regression['MACD Condition'] == 1)  & (btc_hourly_regression['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 25
        btc hourly long trades: 15
        3.4821988916533417


        rsi_basic_profit


        btc_regression_data.columns

Index(['Date', 'Close', 'Volume', ' RSI', 'RSI Condition', 'MACD Condition',
               'EMA Condition', 'Close-EMA200 Price Diff', 'BB Condition', ' ATR',
               'Trend', 'Forecasted', 'Slope', 'Acceleration',
               'Price Relative Close-EMA200 Price Diff', 'Price Relative ATR',
               'future_price1', 'future_price3', 'future_price5', 'future_price10',
               'future_price15', 'future_price30', 'future_price1 P/L',
               'future_price1 % P/L', 'future_price3 P/L', 'future_price3 % P/L',
               'future_price5 P/L', 'future_price5 % P/L', 'future_price10 P/L',
               'future_price10 % P/L', 'future_price15 P/L', 'future_price15 % P/L',
               'future_price30 P/L', 'future_price30 % P/L'],
              dtype='object')


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price1 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price1 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price1 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price1 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price1 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price1 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price1 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price1 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price1 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

1.6819833383226024


        rsi_basic_profit.round(2)


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price3 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price3 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price3 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price3 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price3 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price3 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price3 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price3 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price3 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price3 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price3 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price3 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price3 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price3 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price3 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price3 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price3 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price3 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price3 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price3 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price3 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price3 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price3 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price3 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

1.8419876598880374


        rsi_basic_profit.round(2)


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price5 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price5 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price5 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price5 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price5 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price5 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price5 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price5 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price5 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price5 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price5 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price5 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price5 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price5 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price5 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price5 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price5 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price5 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price5 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price5 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price5 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price5 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price5 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price5 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

2.178472484716973


        rsi_basic_profit.round(2)


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price10 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price10 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price10 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price10 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price10 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price10 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price10 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price10 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price10 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price10 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price10 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price10 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price10 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price10 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price10 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price10 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price10 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price10 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price10 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price10 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price10 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price10 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price10 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price10 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

2.0174055771674912


        rsi_basic_profit.round(2)


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price15 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price15 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price15 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price15 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price15 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price15 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price15 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price15 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price15 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price15 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price15 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price15 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price15 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price15 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price15 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price15 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price15 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price15 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price15 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price15 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price15 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price15 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price15 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price15 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

1.3554341313370308


        rsi_basic_profit.round(2)


        diff = 0.3
        
        btc_long_mean = btc_regression_data['future_price30 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        btc_short_mean = -btc_regression_data['future_price30 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price30 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price30 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        oil_long_mean = oil_regression_data['future_price30 % P/L'].loc[(oil_regression_data['MACD Condition'] == 1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        oil_short_mean = -oil_regression_data['future_price30 % P/L'].loc[(oil_regression_data['MACD Condition'] == -1) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (oil_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        gold_long_mean = gold_regression_data['future_price30 % P/L'].loc[(gold_regression_data['MACD Condition'] == 1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] >0) ].mean()*100
        gold_short_mean = -gold_regression_data['future_price30 % P/L'].loc[(gold_regression_data['MACD Condition'] == -1) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (gold_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sp500_long_mean = sp500_regression_data['future_price30 % P/L'].loc[(sp500_regression_data['MACD Condition'] == 1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sp500_short_mean = -sp500_regression_data['future_price30 % P/L'].loc[(sp500_regression_data['MACD Condition'] == -1) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sp500_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        sti_long_mean = sti_regression_data['future_price30 % P/L'].loc[(sti_regression_data['MACD Condition'] == 1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] < diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] >0)].mean()*100
        sti_short_mean = -sti_regression_data['future_price30 % P/L'].loc[(sti_regression_data['MACD Condition'] == -1) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] > -diff) & (sti_regression_data['Price Relative Close-EMA200 Price Diff'] <0)].mean()*100
        
        btc_long_std = btc_regression_data['future_price30 % P/L'].loc[btc_regression_data['MACD Condition'] == 1].std()
        btc_short_std = btc_regression_data['future_price30 % P/L'].loc[btc_regression_data['MACD Condition'] == -1].std()
        doge_long_std = doge_df_regression_data['future_price30 % P/L'].loc[doge_df_regression_data['MACD Condition'] == 1].std()
        doge_short_std = doge_df_regression_data['future_price30 % P/L'].loc[doge_df_regression_data['MACD Condition'] == -1].std()
        oil_long_std = oil_regression_data['future_price30 % P/L'].loc[oil_regression_data['MACD Condition'] == 1].std()
        oil_short_std = oil_regression_data['future_price30 % P/L'].loc[oil_regression_data['MACD Condition'] == -1].std()
        gold_long_std = gold_regression_data['future_price30 % P/L'].loc[gold_regression_data['MACD Condition'] == 1].std()
        gold_short_std = gold_regression_data['future_price30 % P/L'].loc[gold_regression_data['MACD Condition'] == -1].std()
        sp500_long_std = sp500_regression_data['future_price30 % P/L'].loc[sp500_regression_data['MACD Condition'] == 1].std()
        sp500_short_std = sp500_regression_data['future_price30 % P/L'].loc[sp500_regression_data['MACD Condition'] == -1].std()
        sti_long_std = sti_regression_data['future_price30 % P/L'].loc[sti_regression_data['MACD Condition'] == 1].std()
        sti_short_std = sti_regression_data['future_price30 % P/L'].loc[sti_regression_data['MACD Condition'] == -1].std()
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'Oil Long', 'Oil Short', 'Gold Long', ' Gold Short', 
                     'SP500 Long', 'SP500 Short', 'STI Long', 'STI Short'], 'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,oil_long_mean,oil_short_mean,gold_long_mean,gold_short_mean,sp500_long_mean,sp500_short_mean,sti_long_mean,sti_short_mean], 'Std % Profit': [btc_long_std,btc_short_std,doge_long_std,doge_short_std,oil_long_std,oil_short_std,gold_long_std,gold_short_std,sp500_long_std,sp500_short_std,sti_long_std,sti_short_std]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, ci='Std % Profit', hue='positive',palette = color_dict)
        
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

-0.018360344090962804


        rsi_basic_profit.round(2)


        bnb_df_regression_data = generate_regression_data(bnb_df)
        doge_df_regression_data = generate_regression_data(doge_df)
        sol_df_regression_data = generate_regression_data(sol_df)
        ada_df_regression_data = generate_regression_data(ada_df)
        xrp_df_regression_data = generate_regression_data(xrp_df)
        eth_df_regression_data = generate_regression_data(eth_df)


        diff = 0.2
        volatility = 30
        
        btc_long_mean = btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        btc_short_mean = -btc_regression_data['future_price1 % P/L'].loc[btc_regression_data['MACD Condition'] == -1 & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (btc_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_long_mean = doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        doge_short_mean = -doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == -1) & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (doge_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        bnb_long_mean = bnb_df_regression_data[200:]['future_price1 % P/L'].loc[(bnb_df_regression_data['MACD Condition'] == 1) & (bnb_df_regression_data['Price Relative Close-EMA200 Price Diff'] >diff) & (bnb_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        bnb_short_mean = -bnb_df_regression_data[200:]['future_price1 % P/L'].loc[(bnb_df_regression_data['MACD Condition'] == -1) & (bnb_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (bnb_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sol_long_mean = sol_df_regression_data['future_price1 % P/L'].loc[(sol_df_regression_data['MACD Condition'] == 1) & (sol_df_regression_data['Price Relative Close-EMA200 Price Diff'] >diff)  & (sol_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        sol_short_mean = -sol_df_regression_data['future_price1 % P/L'].loc[(sol_df_regression_data['MACD Condition'] == -1) & (sol_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (sol_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        ada_long_mean = ada_df_regression_data['future_price1 % P/L'].loc[(ada_df_regression_data['MACD Condition'] == 1) & (ada_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (ada_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        ada_short_mean = -ada_df_regression_data['future_price1 % P/L'].loc[(ada_df_regression_data['MACD Condition'] == -1) & (ada_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (ada_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        xrp_long_mean = xrp_df_regression_data['future_price1 % P/L'].loc[(xrp_df_regression_data['MACD Condition'] == 1) & (xrp_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (xrp_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        xrp_short_mean = -xrp_df_regression_data['future_price1 % P/L'].loc[(xrp_df_regression_data['MACD Condition'] == -1) & (xrp_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (xrp_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        eth_long_mean = eth_df_regression_data['future_price1 % P/L'].loc[(eth_df_regression_data['MACD Condition'] == 1) & (eth_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) & (eth_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        eth_short_mean = -eth_df_regression_data['future_price1 % P/L'].loc[(eth_df_regression_data['MACD Condition'] == -1) & (eth_df_regression_data['Price Relative Close-EMA200 Price Diff'] < -diff) & (eth_df_regression_data['Price Relative ATR'] < volatility)].mean()*100
        
        
        d = {'Index':['BTC Long', 'BTC Short', 'DOGE Long', 'DOGE Short', 'BNB Long', 'BNB Short', 'SOL Long', ' SOL Short', 
                     'ADA Long', 'ADA Short', 'XRP Long', 'XRP Short','ETH Long', 'ETH Short'], 
             'Mean % Profit': [btc_long_mean,btc_short_mean,doge_long_mean,doge_short_mean,bnb_long_mean,bnb_short_mean,
                               sol_long_mean,sol_short_mean,ada_long_mean,ada_short_mean,xrp_long_mean,xrp_short_mean,
                              eth_long_mean,eth_short_mean]}
        
        rsi_basic_profit = pd.DataFrame(data=d)
        
        rsi_basic_profit['positive'] = rsi_basic_profit["Mean % Profit"] > 0
        
        color_dict = dict({True:'green',
                          False: 'red'})
        
        fig, ax = plt.subplots(figsize=(14, 15), dpi=80)
        
        ax = sns.barplot(x="Index", y="Mean % Profit", data=rsi_basic_profit, hue='positive',palette = color_dict)
        
        print('btc long trades:', len(btc_regression_data['future_price1 % P/L'].loc[(btc_regression_data['MACD Condition'] == 1) & (btc_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print('doge long trades:',len(doge_df_regression_data['future_price1 % P/L'].loc[(doge_df_regression_data['MACD Condition'] == 1)  & (doge_df_regression_data['Price Relative Close-EMA200 Price Diff'] > diff) ]))
        print(np.nanmean(rsi_basic_profit["Mean % Profit"]))

btc long trades: 13
        doge long trades: 2
        8.33881363605231


        rsi_basic_profit

	Date	Open	High	Low	Close
0	10/08/21	3110.82	3112.81	3112.81	3112.81
1	10/07/21	3099.99	3118.52	3098.81	3101.15
2	10/06/21	3082.58	3091.35	3066.38	3083.88
3	10/05/21	3071.93	3071.93	3047.38	3068.12
4	10/04/21	3079.99	3102.47	3079.62	3089.65
...	...	...	...	...	...
942	01/08/18	3498.62	3514.76	3494.54	3512.18
943	01/05/18	3502.34	3503.68	3480.22	3489.45
944	01/04/18	3476.43	3501.16	3465.95	3501.16
945	01/03/18	3434.70	3468.19	3432.87	3464.28
946	01/02/18	3406.48	3433.82	3403.87	3430.30

	RSI	RSI Condition	Close
count	933.000000	933.000000	947.00000
mean	1497.511185	-0.011790	3084.51773
std	370.906023	0.325705	293.85772
min	378.636955	-1.000000	2233.48000
25%	1232.309343	0.000000	2973.59500
50%	1507.572202	0.000000	3156.28000
75%	1761.470849	0.000000	3247.51500
max	2435.438332	1.000000	3615.28000

	Date	MACD	MACD_SIG	MACD Condition	Close
48	08/02/21	22.884501	22.061499	-1	3161.22
78	06/18/21	-4.643763	-4.378131	1	3144.16
94	05/27/21	9.306626	8.015733	-1	3164.82
104	05/11/21	-9.995296	-6.626359	1	3144.27
117	04/22/21	16.693823	14.232237	-1	3187.78
129	04/06/21	7.638559	8.541848	1	3207.63
130	04/05/21	8.593651	8.552209	-1	3209.74
163	02/16/21	-58.695592	-56.955417	1	2935.34
189	01/08/21	6.562785	1.888375	-1	2993.19
203	12/17/20	-31.689540	-30.421307	1	2858.02
223	11/19/20	-14.055077	-14.396825	-1	2777.00
241	10/26/20	-90.266664	-84.942385	1	2523.31
262	09/25/20	-34.406512	-35.417761	-1	2472.28
263	09/24/20	-35.811041	-35.496417	1	2450.82
296	08/07/20	11.672184	11.499999	-1	2545.51
302	07/29/20	2.400187	5.330226	1	2573.45
320	07/02/20	27.883170	26.799752	-1	2636.69
330	06/18/20	12.121897	12.217050	1	2665.66
332	06/16/20	15.860785	13.300578	-1	2666.85
333	06/15/20	12.572020	13.154867	1	2613.88
341	06/03/20	34.591966	31.669871	-1	2700.39
355	05/13/20	-28.725058	-25.382535	1	2572.01
365	04/27/20	-10.388541	-12.536940	-1	2549.40
369	04/21/20	-14.227936	-13.668312	1	2551.92
376	04/09/20	0.985250	-2.654478	-1	2571.32
393	03/17/20	-45.158100	-43.403953	1	2454.53
415	02/14/20	158.739979	155.281691	-1	3220.03
468	11/28/19	-5.971144	-5.524743	1	3200.61
486	11/04/19	13.879359	13.316145	-1	3236.40
508	10/02/19	-30.703805	-29.430481	1	3103.45
526	09/06/19	5.586083	5.196515	-1	3144.48
537	08/22/19	-16.588957	-14.544033	1	3127.74
562	07/16/19	57.919169	56.524126	-1	3360.03
597	05/27/19	-39.318849	-38.838729	1	3170.77
621	04/18/19	36.843771	35.875586	-1	3347.58
649	03/11/19	-25.401707	-24.970355	1	3191.42
667	02/13/19	4.059838	2.642013	-1	3244.77
681	01/22/19	-14.519160	-13.231035	1	3192.71
689	01/10/19	-7.601291	-7.828079	-1	3183.51
703	12/19/18	-41.543024	-41.025006	1	3058.65
720	11/26/18	-1.354578	-2.683568	-1	3093.38
730	11/12/18	-14.471564	-13.478231	1	3068.15
736	11/01/18	-5.712429	-9.171725	-1	3060.85
743	10/23/18	-20.196717	-18.518421	1	3031.39
765	09/21/18	44.684944	43.832614	-1	3217.68
778	09/04/18	3.230130	5.811819	1	3210.51
788	08/20/18	17.756090	17.317298	-1	3204.71
791	08/15/18	16.213491	16.680341	1	3234.12
805	07/25/18	33.465886	31.569076	-1	3326.83
825	06/27/18	-3.190227	-2.942481	1	3254.77
852	05/17/18	56.528375	54.849182	-1	3536.76
889	03/23/18	-29.634540	-26.599996	1	3421.39
902	03/06/18	3.735910	3.481777	-1	3491.92
905	03/01/18	1.480699	1.808240	1	3513.85
911	02/21/18	9.516414	7.510419	-1	3516.23
922	02/05/18	-19.990364	-17.185425	1	3482.93
936	01/16/18	21.340834	21.283690	-1	3550.21

	Date	EMA5	EMA25	EMA50	EMA100	EMA200	Close	EMA Condition
199	12/23/20	2845.550552	2907.778109	2950.038881	3006.146875	3084.858200	2833.40	-1
200	12/22/20	2839.473701	2901.589024	2945.226376	3002.605749	3082.295631	2827.32	-1
201	12/21/20	2841.822468	2897.352945	2941.355537	2999.514942	3079.949605	2846.52	-1
202	12/18/20	2844.208312	2893.631949	2937.732967	2996.534052	3077.651400	2848.98	-1
203	12/17/20	2848.812208	2890.892568	2934.606968	2993.791200	3075.466013	2858.02	-1
...	...	...	...	...	...	...	...	...
934	01/18/18	3552.298041	3520.351460	3501.564940	3464.786125	3390.491557	3521.31	1
935	01/17/18	3548.835361	3522.009809	3503.147099	3466.313331	3391.998208	3541.91	1
936	01/16/18	3549.293574	3524.179055	3504.992703	3467.974651	3393.572454	3550.21	1
937	01/15/18	3544.999049	3525.119897	3506.224754	3469.329806	3394.993724	3536.41	1
941	01/09/18	3525.558948	3523.660918	3508.215560	3473.197631	3399.880045	3524.65	1

	Date	EMA25	EMA50	EMA100	EMA200	Close	EMA Condition
308	07/21/20	2561.574174	2558.888224	2615.648404	2751.762641	2629.45	-1
309	07/20/20	2565.783853	2561.139666	2615.661307	2750.414754	2616.30	-1
310	07/17/20	2569.837403	2563.388307	2615.717123	2749.101971	2618.48	-1
311	07/16/20	2573.978372	2565.752295	2615.874605	2747.853891	2623.67	-1
312	07/15/20	2579.741574	2569.012989	2616.528574	2746.869275	2648.90	-1
...	...	...	...	...	...	...	...
919	02/08/18	3462.526735	3470.235786	3439.735186	3367.164380	3415.90	1
920	02/07/18	3456.468524	3466.844971	3438.626964	3367.329610	3383.77	1
921	02/06/18	3452.615561	3464.473796	3437.988411	3367.718171	3406.38	1
945	01/03/18	3514.558519	3505.685326	3474.599973	3403.476557	3464.28	1
946	01/02/18	3508.077095	3502.729039	3473.722745	3403.743457	3430.30	1

Stats-Based Crypto-Trading Time Series Models (ADL)

Preamble

RSI (SIGNAL)¶

MACD (SIGNAL)¶

EMA (SIGNAL/CONTROL)¶

ATR (CONTROL)¶

BBANDS (SIGNAL)¶

Generating Regression Data¶

Get Data¶

Windowed Linear Regression and Slope¶

Time Series Model¶

Non-Linear Terms¶

Interaction Terms¶

Testing Strategies¶

RSI¶

MACD¶

	Date	Upper BB	Middle BB	Lower BB	Close	BB Condition
23	09/07/21	3107.548586	3073.3780	3039.207414	3108.53	-1
37	08/18/21	3126.366066	3091.7720	3057.177934	3131.44	-1
39	08/16/21	3138.603051	3097.2395	3055.875949	3145.52	-1
40	08/13/21	3151.540724	3100.5740	3049.607276	3165.49	-1
41	08/12/21	3166.624093	3106.1290	3045.633907	3182.80	-1
...	...	...	...	...	...	...
882	04/04/18	3667.130278	3519.3865	3371.642722	3339.70	1
915	02/14/18	3578.349382	3496.8240	3415.298618	3402.86	1
917	02/12/18	3574.989529	3482.1695	3389.349471	3384.98	1
945	01/03/18	3609.094193	3539.0965	3469.098807	3464.28	1
946	01/02/18	3617.484244	3533.1745	3448.864756	3430.30	1

	Date	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
14	09/20/21	3041.73	0.0	0	0	0	32.930000	0	3071.23	3058.61	3074.31	3101.08	3102.11	3177.18	29.50	16.88	32.58	59.35	60.38	135.45
15	09/17/21	3071.23	0.0	0	0	0	32.685000	0	3064.54	3080.37	3098.80	3083.85	3080.77	3175.10	-6.69	9.14	27.57	12.62	9.54	103.87
16	09/16/21	3064.54	0.0	0	0	0	30.828214	0	3058.61	3074.31	3071.70	3088.84	3109.42	3182.90	-5.93	9.77	7.16	24.30	44.88	118.36
17	09/15/21	3058.61	0.0	0	0	0	29.943342	0	3080.37	3098.80	3068.94	3087.84	3107.49	3149.25	21.76	40.19	10.33	29.23	48.88	90.64
18	09/14/21	3080.37	0.0	0	0	0	29.358817	0	3074.31	3071.70	3108.53	3055.05	3107.62	3161.22	-6.06	-8.67	28.16	-25.32	27.25	80.85
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
912	02/20/18	3476.53	0.0	0	0	0	45.251056	1	3487.88	3402.86	3384.98	3482.93	3577.07	3512.18	11.35	-73.67	-91.55	6.40	100.54	35.65
913	02/19/18	3487.88	0.0	0	0	0	44.438838	1	3443.51	3415.07	3377.24	3529.82	3567.14	3489.45	-44.37	-72.81	-110.64	41.94	79.26	1.57
914	02/15/18	3443.51	0.0	0	0	0	46.433921	1	3402.86	3384.98	3415.90	3547.23	3572.62	3501.16	-40.65	-58.53	-27.61	103.72	129.11	57.65
915	02/14/18	3402.86	0.0	0	0	1	46.020784	1	3415.07	3377.24	3383.77	3533.99	3609.24	3464.28	12.21	-25.62	-19.09	131.13	206.38	61.42
916	02/13/18	3415.07	0.0	0	0	0	45.478585	1	3384.98	3415.90	3406.38	3548.74	3592.08	3430.30	-30.09	0.83	-8.69	133.67	177.01	15.23

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	28.000000	28.000000	28.0	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000	28.000000
mean	3065.048214	-0.035714	-1.0	-0.071429	0.035714	35.796943	0.035714	3039.390000	3025.827857	3024.046071	3020.603929	3008.459286	3048.964643	-25.658214	-39.220357	-41.002143	-44.444286	-56.588929	-16.083571
std	318.714475	0.188982	0.0	0.662687	0.188982	9.765215	0.922241	322.483239	333.169174	321.379377	321.460698	353.519686	308.015070	26.249121	50.755690	67.249032	68.489702	101.420167	169.004492
min	2472.280000	-1.000000	-1.0	-1.000000	0.000000	22.699652	-1.000000	2450.820000	2463.290000	2453.030000	2487.560000	2311.000000	2543.110000	-88.760000	-189.640000	-180.910000	-188.380000	-326.320000	-329.100000
25%	2757.847500	0.000000	-1.0	-0.250000	0.000000	28.342162	-1.000000	2744.912500	2737.052500	2773.602500	2652.332500	2684.075000	2810.662500	-39.997500	-65.122500	-61.732500	-83.407500	-120.900000	-110.570000
50%	3174.165000	0.000000	-1.0	0.000000	0.000000	35.723335	0.000000	3156.565000	3128.615000	3122.745000	3124.275000	3115.710000	3117.580000	-27.290000	-29.405000	-42.545000	-32.750000	-29.635000	-20.635000
75%	3238.492500	0.000000	-1.0	0.000000	0.000000	41.342051	1.000000	3222.425000	3215.820000	3199.272500	3211.895000	3224.060000	3235.110000	-2.767500	-9.432500	-12.520000	9.307500	16.650000	83.482500
max	3536.760000	0.000000	-1.0	1.000000	1.000000	64.668723	1.000000	3533.050000	3562.460000	3540.390000	3575.680000	3568.010000	3569.430000	13.590000	37.360000	127.320000	66.840000	120.270000	540.380000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	11.000000	11.0	11.0	11.000000	11.000000	11.000000	11.0	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000	11.000000
mean	2750.652727	0.0	-1.0	-0.636364	0.090909	39.347001	-1.0	2720.104545	2696.333636	2707.718182	2704.860000	2665.083636	2785.410909	-30.548182	-54.319091	-42.934545	-45.792727	-85.569091	34.758182
std	248.297900	0.0	0.0	0.504525	0.301511	7.333805	0.0	245.600683	239.823212	228.770401	227.754133	281.697610	262.489733	28.251675	60.479219	91.934469	85.454995	135.082905	199.423848
min	2472.280000	0.0	-1.0	-1.000000	0.000000	26.288926	-1.0	2450.820000	2463.290000	2453.030000	2487.560000	2311.000000	2543.110000	-88.760000	-189.640000	-180.910000	-188.380000	-326.320000	-233.890000
25%	2560.360000	0.0	-1.0	-1.000000	0.000000	35.155502	-1.0	2549.270000	2513.225000	2524.650000	2574.290000	2480.160000	2597.960000	-41.470000	-80.010000	-91.695000	-101.410000	-149.040000	-80.270000
50%	2666.850000	0.0	-1.0	-1.000000	0.000000	40.250374	-1.0	2611.630000	2574.100000	2597.850000	2611.630000	2587.810000	2628.620000	-31.240000	-54.730000	-46.540000	-25.230000	-112.580000	-24.210000
75%	2918.925000	0.0	-1.0	0.000000	0.000000	42.037396	-1.0	2903.695000	2864.770000	2903.505000	2838.405000	2874.875000	3079.785000	-23.450000	-18.995000	4.870000	17.830000	30.185000	96.820000
max	3183.510000	0.0	-1.0	0.000000	1.000000	51.300404	-1.0	3158.070000	3102.800000	3065.070000	3069.670000	3116.390000	3180.430000	13.590000	37.360000	127.320000	66.840000	78.160000	540.380000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	27.000000	27.0	27.0	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000	27.000000
mean	2980.538148	0.0	1.0	0.111111	0.037037	37.586677	-0.111111	2999.415926	3013.316667	3026.528148	3021.459259	3039.157407	3026.689259	18.877778	32.778519	45.990000	40.921111	58.619259	46.151111
std	312.365448	0.0	0.0	0.423659	0.192450	15.961500	0.933700	308.364691	303.593496	292.246799	311.520715	336.434094	324.965521	22.686305	57.541186	80.737257	128.564614	169.484165	190.115287
min	2450.820000	0.0	1.0	-1.000000	0.000000	20.193016	-1.000000	2481.140000	2485.710000	2500.780000	2470.590000	2416.240000	2482.550000	-13.110000	-51.780000	-33.060000	-110.990000	-150.420000	-326.420000
25%	2639.770000	0.0	1.0	0.000000	0.000000	26.958589	-1.000000	2677.125000	2739.605000	2810.965000	2764.770000	2759.450000	2724.610000	3.035000	4.180000	1.635000	-37.350000	-5.860000	-42.005000
50%	3103.450000	0.0	1.0	0.000000	0.000000	38.696086	0.000000	3122.570000	3125.630000	3125.820000	3151.040000	3158.240000	3122.570000	14.080000	21.730000	34.690000	41.270000	38.310000	9.430000
75%	3196.660000	0.0	1.0	0.000000	0.000000	41.638960	1.000000	3208.470000	3217.535000	3208.880000	3223.140000	3275.845000	3246.475000	34.230000	36.585000	54.810000	78.190000	86.540000	119.275000
max	3513.850000	0.0	1.0	1.000000	1.000000	105.759356	1.000000	3517.940000	3555.850000	3512.140000	3485.570000	3483.160000	3562.460000	70.750000	224.110000	378.010000	565.030000	703.710000	702.040000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	10.00000	10.0	10.0	10.000000	10.0	10.000000	10.0	10.000000	10.000000	10.00000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	3242.86300	0.0	1.0	0.400000	0.0	32.928918	1.0	3259.014000	3274.775000	3280.47100	3295.869000	3338.316000	3315.912000	16.151000	31.912000	37.608000	53.006000	95.453000	73.049000
std	128.62053	0.0	0.0	0.516398	0.0	8.840026	0.0	137.298721	147.666712	135.33644	111.564947	95.793959	154.486478	23.937599	25.659124	42.677631	74.978376	109.381312	115.358465
min	3103.45000	0.0	1.0	0.000000	0.0	22.969980	1.0	3122.570000	3125.630000	3125.82000	3166.840000	3204.520000	3122.570000	-5.170000	0.710000	-25.390000	-110.990000	-130.080000	-65.900000
25%	3175.93250	0.0	1.0	0.000000	0.0	26.000642	1.0	3176.385000	3192.512500	3195.52250	3211.495000	3270.907500	3202.022500	0.362500	16.412500	4.355000	34.112500	57.990000	-0.072500
50%	3205.56000	0.0	1.0	0.000000	0.0	30.962074	1.0	3211.365000	3224.280000	3249.31500	3272.925000	3321.595000	3312.740000	6.600000	26.800000	35.865000	63.785000	79.830000	23.590000
75%	3249.60750	0.0	1.0	1.000000	0.0	39.598766	1.0	3271.370000	3286.745000	3333.97500	3382.107500	3390.160000	3394.290000	23.305000	39.657500	60.762500	84.530000	191.407500	139.490000
max	3513.85000	0.0	1.0	1.000000	0.0	47.524629	1.0	3517.940000	3555.850000	3512.14000	3485.570000	3483.160000	3562.460000	69.980000	91.920000	105.880000	175.920000	228.390000	307.690000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	44.000000	44.0	44.0	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000	44.000000
mean	2917.178636	1.0	0.0	-0.431818	0.477273	36.420665	-0.204545	2913.642045	2903.369318	2896.085682	2905.350227	2929.480682	2940.895455	-3.536591	-13.809318	-21.092955	-11.828409	12.302045	23.716818
std	256.653071	0.0	0.0	0.501056	0.505258	7.977353	0.823479	264.767901	274.931938	282.932416	287.958088	294.138856	337.611232	31.610656	58.736073	78.083901	96.780293	93.931161	119.961688
min	2423.840000	1.0	0.0	-1.000000	0.000000	26.375988	-1.000000	2423.840000	2423.840000	2423.840000	2423.840000	2523.620000	2450.820000	-95.640000	-145.490000	-207.780000	-287.550000	-187.670000	-246.660000
25%	2711.772500	1.0	0.0	-1.000000	0.000000	29.168108	-1.000000	2710.175000	2604.175000	2568.357500	2542.025000	2554.797500	2500.770000	-19.495000	-47.010000	-68.887500	-43.035000	-45.762500	-23.917500
50%	2973.705000	1.0	0.0	0.000000	0.000000	34.204931	0.000000	2973.270000	2942.190000	2928.680000	2929.435000	2926.655000	2997.345000	-2.340000	-2.000000	-1.255000	9.065000	29.725000	26.255000
75%	3142.527500	1.0	0.0	0.000000	1.000000	42.298924	0.250000	3142.527500	3142.527500	3161.360000	3173.202500	3219.787500	3240.997500	24.685000	30.632500	31.127500	40.747500	68.727500	78.702500
max	3339.700000	1.0	0.0	0.000000	1.000000	51.686716	1.000000	3412.150000	3427.970000	3439.350000	3513.310000	3553.730000	3476.530000	72.450000	89.030000	113.550000	173.610000	214.030000	225.180000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	55.000000	55.0	55.000000	55.000000	55.000000	55.000000	55.0	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000	55.000000
mean	3301.093818	-1.0	-0.018182	0.963636	-0.381818	50.279923	1.0	3298.715091	3297.680909	3303.373455	3297.039091	3296.875273	3268.852909	-2.378727	-3.412909	2.279636	-4.054727	-4.218545	-32.240909
std	188.755738	0.0	0.134840	0.188919	0.490310	23.791498	0.0	187.744452	195.742925	209.415855	231.614786	228.134477	222.795498	23.990542	46.162591	65.428661	96.994312	98.626907	139.132968
min	2794.170000	-1.0	-1.000000	0.000000	-1.000000	25.593255	1.0	2751.500000	2700.390000	2550.860000	2499.830000	2523.550000	2518.160000	-54.660000	-115.240000	-246.110000	-297.140000	-273.420000	-282.410000
25%	3213.355000	-1.0	0.000000	1.000000	-1.000000	33.677142	1.0	3206.565000	3205.840000	3213.355000	3197.655000	3221.730000	3212.530000	-16.475000	-41.095000	-26.545000	-45.510000	-59.985000	-160.650000
50%	3346.390000	-1.0	0.000000	1.000000	0.000000	43.504595	1.0	3350.540000	3356.950000	3353.470000	3334.230000	3325.600000	3247.860000	0.060000	-7.530000	2.210000	0.450000	3.050000	-17.550000
75%	3418.855000	-1.0	0.000000	1.000000	0.000000	61.071969	1.0	3417.265000	3417.265000	3417.265000	3437.115000	3411.110000	3352.090000	14.145000	24.995000	36.855000	59.365000	52.730000	78.335000
max	3615.280000	-1.0	0.000000	1.000000	0.000000	117.485150	1.0	3613.930000	3570.020000	3584.560000	3570.170000	3598.730000	3598.730000	49.790000	92.140000	138.680000	188.680000	201.820000	260.530000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	70.000000	70.000000	70.000000	70.000000	70.0	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000	70.000000
mean	2986.430714	0.300000	0.000000	-0.371429	1.0	35.702044	0.000000	2987.270857	2981.850286	2977.725143	2983.465143	3011.833143	3056.373000	0.840143	-4.580429	-8.705571	-2.965571	25.402429	69.942286
std	311.120339	0.461566	0.170251	0.486675	0.0	12.624611	0.868115	311.808179	309.626580	302.923491	311.104642	298.080561	307.476354	39.272759	53.538780	73.277841	132.878205	165.010155	219.831930
min	2233.480000	0.000000	-1.000000	-1.000000	1.0	21.622012	-1.000000	2233.480000	2311.000000	2416.240000	2233.480000	2450.680000	2463.290000	-128.570000	-110.060000	-166.230000	-304.870000	-326.320000	-233.890000
25%	2797.670000	0.000000	0.000000	-1.000000	1.0	27.047908	-1.000000	2806.167500	2771.952500	2743.960000	2836.035000	2867.685000	2932.367500	-17.672500	-30.270000	-54.307500	-79.570000	-65.617500	-33.140000
50%	3086.225000	0.000000	0.000000	0.000000	1.0	32.036454	0.000000	3092.880000	3063.995000	3068.140000	3084.600000	3107.265000	3143.340000	-0.135000	-10.700000	-14.680000	-8.180000	24.815000	34.585000
75%	3187.090000	1.000000	0.000000	0.000000	1.0	40.845653	1.000000	3193.747500	3191.762500	3180.460000	3197.337500	3193.385000	3243.512500	14.032500	21.395000	16.362500	45.447500	84.727500	110.515000
max	3498.200000	1.000000	1.000000	0.000000	1.0	86.987863	1.000000	3501.300000	3479.760000	3466.380000	3533.990000	3609.240000	3517.940000	177.260000	192.140000	262.290000	548.890000	774.240000	929.670000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	26.000000	26.000000	26.0	26.0	26.0	26.000000	26.0	26.000000	26.0000	26.000000	26.000000	26.000000	26.000000	26.000000	26.000000	26.00000	26.000000	26.000000	26.000000
mean	3251.492692	0.192308	0.0	0.0	1.0	30.658029	1.0	3253.615385	3247.4100	3238.022692	3239.091538	3257.128077	3330.798077	2.122692	-4.082692	-13.47000	-12.401154	5.635385	79.305385
std	124.961705	0.401918	0.0	0.0	0.0	7.281805	0.0	125.233842	125.4639	119.086056	137.790410	153.277721	117.242815	28.132920	33.941365	43.16825	82.448464	108.827701	115.604817
min	3056.470000	0.000000	0.0	0.0	1.0	22.280093	1.0	3067.520000	3056.4700	3065.330000	3078.360000	3087.970000	3144.480000	-65.950000	-76.200000	-79.50000	-115.270000	-180.810000	-102.840000
25%	3169.877500	0.000000	0.0	0.0	1.0	26.697141	1.0	3167.122500	3176.3725	3161.415000	3137.712500	3162.112500	3235.645000	-8.892500	-24.380000	-40.00250	-84.280000	-67.852500	18.117500
50%	3217.755000	0.000000	0.0	0.0	1.0	28.519650	1.0	3216.770000	3208.6600	3208.620000	3207.795000	3195.190000	3310.725000	3.565000	-12.825000	-18.36000	-36.675000	-2.770000	47.855000
75%	3317.275000	0.000000	0.0	0.0	1.0	36.104778	1.0	3324.332500	3319.6775	3313.092500	3271.207500	3327.265000	3429.007500	12.152500	13.107500	-0.19500	45.230000	71.205000	149.377500
max	3498.200000	1.000000	0.0	0.0	1.0	46.020784	1.0	3501.300000	3479.7600	3466.380000	3533.990000	3609.240000	3517.940000	72.450000	88.270000	99.65000	173.610000	214.030000	304.450000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
mean	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
std	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
min	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
25%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
50%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
75%	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
max	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	61.000000	61.000000	61.0	61.000000	61.0	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000
mean	3147.171639	-0.344262	0.0	0.672131	-1.0	41.129853	0.540984	3149.098689	3156.505902	3161.389344	3149.882295	3135.338525	3144.384426	1.927049	9.334262	14.217705	2.710656	-11.833115	-2.787213
std	251.206177	0.479070	0.0	0.473333	0.0	23.673062	0.720504	252.122987	258.603112	267.813808	274.422449	281.319024	282.963494	30.517524	59.343061	92.213283	131.787919	156.868024	166.199884
min	2531.790000	-1.000000	0.0	0.000000	-1.0	18.973271	-1.000000	2538.550000	2527.920000	2519.810000	2499.830000	2484.910000	2518.160000	-73.010000	-114.630000	-246.110000	-297.140000	-394.410000	-323.980000
25%	3017.150000	-1.000000	0.0	0.000000	-1.0	27.563405	0.000000	3018.270000	3018.270000	3025.030000	3082.960000	3068.150000	3078.360000	-16.530000	-31.110000	-34.390000	-66.080000	-100.360000	-101.790000
50%	3203.930000	0.000000	0.0	1.000000	-1.0	36.124680	1.000000	3207.360000	3209.790000	3236.260000	3181.030000	3176.570000	3198.650000	2.800000	6.030000	4.670000	1.560000	-44.810000	-36.640000
75%	3300.750000	0.000000	0.0	1.000000	-1.0	44.185695	1.000000	3300.750000	3346.390000	3362.430000	3348.640000	3329.460000	3254.770000	17.880000	40.830000	67.690000	64.120000	58.700000	89.260000
max	3615.280000	0.000000	0.0	1.000000	-1.0	119.844777	1.000000	3613.930000	3570.020000	3584.560000	3518.480000	3548.230000	3615.280000	105.080000	185.730000	339.630000	433.060000	520.040000	492.040000

	Close	RSI Condition	MACD Condition	EMA Condition	BB Condition	ATR	Trend	future_price1	future_price3	future_price5	future_price10	future_price15	future_price30	future_price1 P/L	future_price3 P/L	future_price5 P/L	future_price10 P/L	future_price15 P/L	future_price30 P/L
count	8.000000	8.0	8.0	8.0	8.0	8.000000	8.0	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000	8.000000
mean	2821.693750	0.0	0.0	0.0	-1.0	32.003612	-1.0	2819.261250	2794.926250	2798.562500	2778.227500	2755.513750	2799.190000	-2.432500	-26.767500	-23.131250	-43.466250	-66.180000	-22.503750
std	296.580805	0.0	0.0	0.0	0.0	10.787601	0.0	312.146218	303.083263	307.656373	280.359568	284.648733	271.893036	27.369196	55.968789	76.451593	96.898914	150.985435	151.998573
min	2531.790000	0.0	0.0	0.0	-1.0	18.973271	-1.0	2538.550000	2527.920000	2519.810000	2527.920000	2484.910000	2567.650000	-42.850000	-114.630000	-113.080000	-186.630000	-394.410000	-323.980000
25%	2554.257500	0.0	0.0	0.0	-1.0	23.159769	-1.0	2539.607500	2537.850000	2539.732500	2556.817500	2546.705000	2605.750000	-23.507500	-52.432500	-66.807500	-123.152500	-113.095000	-98.430000
50%	2743.800000	0.0	0.0	0.0	-1.0	31.127838	-1.0	2705.990000	2661.255000	2670.820000	2643.985000	2612.710000	2641.050000	3.380000	-34.710000	-36.710000	-13.940000	-25.430000	27.245000
75%	3137.590000	0.0	0.0	0.0	-1.0	42.536336	-1.0	3166.897500	3111.482500	3091.420000	3029.905000	3045.792500	3043.057500	14.610000	-0.052500	2.620000	2.372500	36.382500	94.630000
max	3167.790000	0.0	0.0	0.0	-1.0	45.486099	-1.0	3190.620000	3209.790000	3267.400000	3239.100000	3176.570000	3243.920000	35.120000	78.310000	135.920000	107.620000	64.180000	112.440000

	Date	Open	High	Low	Close	Volume
0	1987-12-28	824.40	824.40	824.40	824.40	NaN
1	1987-12-29	810.90	810.90	810.90	810.90	NaN
2	1987-12-30	823.20	823.20	823.20	823.20	NaN
3	1988-01-04	833.60	833.60	833.60	833.60	NaN
4	1988-01-05	879.30	879.30	879.30	879.30	NaN
...	...	...	...	...	...	...
8453	2021-11-03	3234.28	3236.86	3216.86	3219.69	204587761.0
8454	2021-11-05	3234.41	3248.07	3225.16	3242.34	279049200.0
8455	2021-11-08	3251.85	3270.65	3250.39	3263.90	319638000.0
8456	2021-11-09	3267.92	3273.54	3239.71	3243.42	332843200.0
8457	2021-11-10	3245.19	3246.00	3218.31	3231.32	NaN

	Date	Open	High	Low	Close	Volume
0	1/1/2015	4053	4053	4053	4053	1082572
1	2/1/2015	4053	5164	4006	4103	1082610
2	3/1/2015	4103	4935	4056	4935	1082642
3	4/1/2015	4935	5247	4226	4249	1082694
4	5/1/2015	4249	5335	4126	4137	1082745
...	...	...	...	...	...	...
2778	10/8/2022	3398	3514	2375	2613	1184552
2779	11/8/2022	2613	2990	2471	2601	1184602
2780	12/8/2022	2601	4041	2473	2697	1184634
2781	13/8/2022	2697	4377	2697	3470	1184650
2782	14/8/2022	3470	4793	3121	3161	1184669

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	32.000000	3.200000e+01	32.000000	32.000000	32.0	32.000000	32.000000	32.000000	32.000000	32.0	...	32.000000	32.000000	32.000000	32.000000	32.000000	32.000000	32.000000	32.000000	32.000000	32.000000
mean	4008.877690	6.264674e+09	57.447237	-0.062500	1.0	0.750000	1198.619438	-0.062500	234.856549	1.0	...	51.972857	0.033109	40.089586	0.041386	107.969098	0.069483	119.937623	0.084667	454.422790	0.174932
std	4530.571752	9.783652e+09	10.045499	0.245935	0.0	0.439941	1907.217346	0.245935	380.113580	0.0	...	588.652397	0.070953	799.965292	0.096019	1233.418409	0.142010	1337.147609	0.160539	2892.189093	0.352368
min	293.623993	3.059200e+07	39.035522	-1.000000	1.0	0.000000	23.184583	-1.000000	4.845750	1.0	...	-2259.400391	-0.132798	-2955.549805	-0.250761	-4165.000000	-0.353376	-4530.299805	-0.259921	-9152.490234	-0.525115
25%	585.029022	5.999460e+07	51.167565	0.000000	1.0	0.750000	57.837228	0.000000	12.622950	1.0	...	1.175255	0.002166	3.317490	0.006163	3.753242	0.005551	-5.878281	-0.002678	-17.090798	-0.002641
50%	1040.425049	2.307740e+08	59.961742	0.000000	1.0	1.000000	223.330091	0.000000	54.426280	1.0	...	23.459503	0.025814	61.738998	0.022342	50.430115	0.075629	61.507004	0.066150	45.614990	0.104992
75%	7916.903442	1.055410e+10	65.083361	0.000000	1.0	1.000000	1999.167047	0.000000	293.940517	1.0	...	233.744324	0.064248	303.356506	0.103796	477.322570	0.159274	286.520935	0.205025	232.644233	0.277055
max	17429.500000	2.989318e+10	75.897147	0.000000	1.0	1.000000	9545.651620	0.000000	1702.536466	1.0	...	947.970215	0.236071	1249.420410	0.211764	2317.269531	0.354806	3265.538086	0.394313	11625.710450	1.476901

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	18.000000	1.800000e+01	18.000000	18.000000	18.0	18.000000	18.000000	18.000000	18.000000	18.0	...	18.000000	18.000000	18.000000	18.000000	18.000000	18.000000	18.000000	18.000000	18.000000	18.000000
mean	4955.405020	6.425943e+09	43.008671	0.166667	-1.0	-0.611111	-1041.054754	0.166667	187.523029	-1.0	...	-94.266882	-0.018582	-112.793791	-0.022014	-142.534587	-0.022471	-108.652961	-0.007967	-87.194437	0.005086
std	2653.460727	5.945564e+09	9.739398	0.383482	0.0	0.501631	788.013232	0.383482	156.756870	0.0	...	562.411220	0.078433	604.386099	0.084543	856.929989	0.129672	959.132313	0.155424	1159.337266	0.192402
min	236.802002	1.113430e+07	26.108776	0.000000	-1.0	-1.000000	-2966.615892	0.000000	4.139630	-1.0	...	-1043.700196	-0.127135	-1235.870606	-0.150543	-2005.330079	-0.314746	-2550.550049	-0.400321	-2884.319825	-0.452707
25%	3560.557496	3.593760e+09	39.145753	0.000000	-1.0	-1.000000	-1603.517074	0.000000	86.066374	-1.0	...	-334.422241	-0.056195	-299.072388	-0.074687	-225.281983	-0.069916	-113.650024	-0.052682	-213.548095	-0.030023
50%	6428.830079	5.405500e+09	42.429902	0.000000	-1.0	-1.000000	-982.901602	0.000000	157.699586	-1.0	...	-88.358520	-0.034405	-68.869751	-0.025324	-10.729981	-0.008233	-11.218002	-0.017168	13.004006	0.029656
75%	6771.950074	6.635868e+09	49.745035	0.000000	-1.0	0.000000	-651.448766	0.000000	264.541367	-1.0	...	9.329758	-0.000336	9.144192	0.006603	95.137268	0.023550	247.284057	0.036129	414.868408	0.088411
max	8209.400391	2.194288e+10	59.848159	1.000000	-1.0	0.000000	-41.819634	1.000000	647.709040	-1.0	...	1730.300781	0.230256	1741.476563	0.231744	1810.045898	0.240868	1752.889648	0.288186	1874.530273	0.337859

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	5.000000	5.000000e+00	5.000000	5.0	5.0	5.0	5.000000	5.0	5.000000	5.0	...	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000	5.000000
mean	1599.085181	1.075414e+09	26.866047	1.0	0.0	0.0	213.828033	1.0	131.972490	1.0	...	228.394965	0.117370	485.564838	0.254419	354.525830	0.183088	550.326990	0.254824	1464.591437	0.668475
std	1153.304319	1.014302e+09	1.875510	0.0	0.0	0.0	203.433908	0.0	121.257148	0.0	...	189.779714	0.057709	398.160044	0.141672	294.017088	0.113423	492.930312	0.203150	1315.895308	0.551777
min	364.330994	1.533510e+08	23.805647	1.0	0.0	0.0	24.670983	1.0	16.188372	1.0	...	22.837005	0.050374	45.224975	0.082608	27.395019	0.073236	13.924011	0.038218	24.837952	0.045369
25%	547.465027	3.309330e+08	26.569021	1.0	0.0	0.0	27.744906	1.0	21.117980	1.0	...	27.578003	0.062682	55.899017	0.153429	40.093994	0.075193	25.750976	0.047037	42.899017	0.117747
50%	1929.819946	9.936080e+08	27.229018	1.0	0.0	0.0	216.063673	1.0	153.683627	1.0	...	320.019898	0.135638	737.940064	0.244067	527.890137	0.167321	758.319947	0.319536	2252.110230	0.848457
75%	1998.859985	1.182870e+09	28.343900	1.0	0.0	0.0	287.275206	1.0	158.116224	1.0	...	343.609986	0.160101	770.020020	0.382388	577.619995	0.288975	945.520142	0.379376	2326.269898	1.163798
max	3154.949951	2.716310e+09	28.382649	1.0	0.0	0.0	513.385397	1.0	310.756245	1.0	...	427.929932	0.178053	818.740113	0.409604	599.630005	0.310718	1008.119873	0.489953	2676.840088	1.167005

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	10.000000	1.000000e+01	10.000000	10.0	10.0	10.0	10.000000	10.000000	10.000000	10.0	...	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	2507.265637	4.069447e+09	71.876849	-1.0	0.0	0.0	-408.318536	-0.800000	62.866431	-1.0	...	1.577239	-0.007711	-20.494342	-0.007223	-81.140655	-0.001887	-120.498265	0.024721	-120.432251	0.055084
std	2618.338700	4.420388e+09	1.866423	0.0	0.0	0.0	476.647104	0.421637	79.704864	0.0	...	141.378076	0.034063	201.835466	0.044251	106.659095	0.058016	303.048628	0.108544	432.629598	0.126253
min	249.007004	2.522350e+07	70.159186	-1.0	0.0	0.0	-1050.347793	-1.000000	4.104796	-1.0	...	-288.168945	-0.069564	-287.741455	-0.069460	-246.151855	-0.060908	-957.989746	-0.124235	-1334.399902	-0.173049
25%	251.751255	4.217360e+07	70.446502	-1.0	0.0	0.0	-925.735403	-1.000000	4.951041	-1.0	...	-6.624244	-0.025470	-131.781311	-0.033289	-141.821350	-0.032665	-77.040100	-0.019297	18.518128	0.004625
50%	2105.083237	2.588507e+09	71.021277	-1.0	0.0	0.0	-109.624556	-1.000000	44.152073	-1.0	...	-4.567498	-0.018334	-5.831001	-0.023312	-47.405876	-0.020420	-18.042442	0.007022	25.271996	0.057267
75%	4004.100097	8.476662e+09	73.074716	-1.0	0.0	0.0	-20.305267	-1.000000	86.684132	-1.0	...	45.654846	0.025297	6.057254	0.022905	1.280505	0.005137	7.626747	0.030454	29.439487	0.107288
max	7711.109863	9.933627e+09	75.720923	-1.0	0.0	0.0	-0.923997	0.000000	257.879984	-1.0	...	240.470215	0.035678	481.040528	0.062383	29.359986	0.115445	73.695008	0.289773	82.232422	0.309004

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	68.000000	6.800000e+01	68.000000	68.0	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000	...	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000	68.000000
mean	5299.142319	1.321458e+10	24.044565	1.0	0.000000	-0.926471	-1703.158490	0.500000	312.973478	-0.852941	...	76.785134	0.019628	112.822486	0.033694	121.790158	0.033609	136.932781	0.038967	369.499937	0.094705
std	2317.064692	1.420609e+10	5.293810	0.0	0.299253	0.262944	1139.869550	0.503718	187.810513	0.525888	...	540.557564	0.097588	648.372233	0.135249	841.827910	0.159462	932.089062	0.180959	1191.058873	0.281503
min	210.494995	3.171900e+07	9.920239	1.0	-1.000000	-1.000000	-3606.390786	0.000000	9.027204	-1.000000	...	-1102.459961	-0.198487	-1276.430176	-0.226980	-1857.590088	-0.323715	-1729.409180	-0.307530	-2495.865235	-0.434945
25%	3695.771607	4.673220e+09	21.706389	1.0	0.000000	-1.000000	-2876.402716	0.000000	237.375686	-1.000000	...	-182.582459	-0.031006	-254.322571	-0.048948	-260.586731	-0.054793	-338.962769	-0.069086	-173.034668	-0.039104
50%	5473.322509	6.651270e+09	26.142586	1.0	0.000000	-1.000000	-1669.241149	0.500000	297.658078	-1.000000	...	17.882004	0.004975	50.561996	0.018353	33.744507	0.018874	79.900390	0.027658	206.425171	0.084911
75%	7237.922730	1.765116e+10	28.347218	1.0	0.000000	-1.000000	-644.950077	1.000000	352.389357	-1.000000	...	317.734100	0.070279	435.115357	0.105747	551.092499	0.141672	582.004211	0.152273	1159.781616	0.157637
max	8620.566406	7.415677e+10	29.855741	1.0	1.000000	0.000000	513.385397	1.000000	1278.115935	1.000000	...	2058.226074	0.274669	1934.198730	0.409604	3211.130371	0.461683	4448.430175	0.639577	3010.300293	1.167005

	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	Trend	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
count	274.000000	2.740000e+02	274.000000	274.0	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	...	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000	274.000000
mean	4058.478697	6.424169e+09	77.691395	-1.0	-0.010949	0.952555	1588.399032	-0.452555	193.222888	0.927007	...	107.984713	0.024476	146.245628	0.035041	257.549266	0.061185	343.972050	0.086242	453.721767	0.129587
std	4371.948922	1.042943e+10	5.958542	0.0	0.159752	0.212978	2599.533721	0.498655	297.823456	0.375730	...	716.924367	0.085323	956.722235	0.111500	1368.752056	0.165980	1663.512352	0.201583	1904.240876	0.271083
min	249.007004	2.522350e+07	70.008664	-1.0	-1.000000	0.000000	-1050.347793	-1.000000	4.104796	-1.000000	...	-3311.298828	-0.219520	-5309.000976	-0.277366	-4507.699219	-0.291252	-5483.600586	-0.308928	-7925.599610	-0.513061
25%	661.387009	1.178695e+08	72.380393	-1.0	0.000000	1.000000	153.035214	-1.000000	17.290481	1.000000	...	-24.103347	-0.021174	-95.660583	-0.030469	-45.932281	-0.043047	-37.698250	-0.037118	-65.449249	-0.060397
50%	1843.829956	1.072380e+09	76.791372	-1.0	0.000000	1.000000	480.064528	0.000000	78.825818	1.000000	...	16.244262	0.017853	22.832032	0.033283	38.719513	0.024584	59.368744	0.056319	57.273010	0.103112
75%	6001.929321	8.865037e+09	81.656052	-1.0	0.000000	1.000000	2067.131153	0.000000	261.947094	1.000000	...	134.743408	0.068128	161.178528	0.089885	206.206300	0.119507	461.255493	0.193865	910.109620	0.275052
max	19497.400391	4.510573e+10	94.302215	-1.0	1.000000	1.000000	13291.593648	0.000000	1515.145714	1.000000	...	6242.499024	0.535506	6825.099610	0.616284	8081.349610	0.823086	8521.800782	0.815199	10755.319336	1.505424

	Date	Close	Forecasted
199	2015-04-04	253.697006	NaN
200	2015-04-05	260.597992	NaN
201	2015-04-06	255.492004	NaN
202	2015-04-07	253.179993	NaN
203	2015-04-08	245.022003	NaN
...	...	...	...
2031	2020-04-09	7302.089355	7223.577427
2032	2020-04-10	6865.493164	7267.306952
2033	2020-04-11	6859.083008	7309.345097
2035	2020-04-13	6845.037598	7299.302123
2037	2020-04-15	6642.109863	7178.890899

	lry	ibo	ide
period
1974-01-01	5.903658	0.154736	0.094000
1974-04-01	5.873820	0.177991	0.095500
1974-07-01	5.837818	0.170565	0.095500
1974-10-01	5.812255	0.152227	0.095500
1975-01-01	5.803945	0.134228	0.088500
1975-04-01	5.786761	0.133481	0.079000
1975-07-01	5.833412	0.128413	0.076000
1975-10-01	5.929849	0.128836	0.074000
1976-01-01	5.937654	0.141345	0.072000
1976-04-01	5.935332	0.153119	0.078000
1976-07-01	5.931755	0.160579	0.080000
1976-10-01	5.940524	0.161832	0.103000
1977-01-01	5.928044	0.166581	0.097000
1977-04-01	5.937180	0.163021	0.088000
1977-07-01	5.956553	0.169247	0.095000
1977-10-01	5.939602	0.172890	0.097000
1978-01-01	5.932408	0.171737	0.099000
1978-04-01	5.931238	0.175707	0.088000
1978-07-01	5.972136	0.171193	0.081000
1978-10-01	5.968729	0.182004	0.077000
1979-01-01	5.962473	0.170043	0.075000
1979-04-01	5.986486	0.168929	0.077000
1979-07-01	5.984788	0.178275	0.086000
1979-10-01	5.977044	0.180446	0.101000
1980-01-01	5.985101	0.191067	0.109000
1980-04-01	5.945198	0.196700	0.121000
1980-07-01	5.901625	0.192300	0.121000
1980-10-01	5.902440	0.182967	0.107000
1981-01-01	5.897422	0.184533	0.105000
1981-04-01	5.893866	0.192867	0.109000
1981-07-01	5.883451	0.203233	0.111000
1981-10-01	5.894687	0.192333	0.109000
1982-01-01	5.902336	0.203267	0.107000
1982-04-01	5.924388	0.210767	0.111000
1982-07-01	5.949907	0.208867	0.111000
1982-10-01	5.928668	0.197067	0.110000
1983-01-01	5.915890	0.161100	0.106000
1983-04-01	5.938670	0.138300	0.087000
1983-07-01	5.932186	0.141700	0.083000
1983-10-01	5.974309	0.133800	0.085000
1984-01-01	5.963916	0.134100	0.085000
1984-04-01	6.000994	0.141700	0.083000
1984-07-01	5.990538	0.144800	0.085000
1984-10-01	5.968702	0.141200	0.091647
1985-01-01	6.013800	0.131700	0.090265
1985-04-01	6.003318	0.119600	0.087635
1985-07-01	6.044517	0.107000	0.080043
1985-10-01	6.081291	0.103700	0.075584
1986-01-01	6.095570	0.097700	0.072379
1986-04-01	6.089658	0.098400	0.067458
1986-07-01	6.098992	0.111500	0.067941
1986-10-01	6.080706	0.114267	0.075396
1987-01-01	6.061175	0.119333	0.076653
1987-04-01	6.063730	0.117333	0.076259
1987-07-01	6.050830	0.118967	0.075163

Dep. Variable:	lrm	No. Observations:	55
Model:	ARDL(3, 3, 3, 3)	Log Likelihood	140.153
Method:	Conditional MLE	S.D. of innovations	0.016
Date:	Wed, 17 Nov 2021	AIC	-246.306
Time:	12:01:01	BIC	-213.135
Sample:	10-01-1974	HQIC	-233.589
	- 07-01-1987

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	2.6203	0.584	4.483	0.000	1.435	3.806
lrm.L1	0.2944	0.155	1.903	0.065	-0.019	0.608
lrm.L2	0.5407	0.138	3.919	0.000	0.261	0.820
lrm.L3	-0.2584	0.134	-1.924	0.062	-0.531	0.014
lry.L0	0.6649	0.135	4.919	0.000	0.391	0.939
lry.L1	-0.2144	0.200	-1.073	0.290	-0.620	0.191
lry.L2	-0.0673	0.194	-0.347	0.730	-0.460	0.326
lry.L3	0.0447	0.155	0.289	0.774	-0.269	0.359
ibo.L0	-1.1219	0.354	-3.165	0.003	-1.841	-0.403
ibo.L1	-0.0296	0.663	-0.045	0.965	-1.374	1.315
ibo.L2	0.2669	0.616	0.433	0.667	-0.983	1.517
ibo.L3	-1.0704	0.420	-2.546	0.015	-1.923	-0.218
ide.L0	0.1248	0.598	0.209	0.836	-1.087	1.337
ide.L1	-0.2093	0.753	-0.278	0.783	-1.736	1.317
ide.L2	0.9826	0.733	1.340	0.189	-0.505	2.470
ide.L3	0.4379	0.505	0.867	0.392	-0.586	1.462

Dep. Variable:	future_price1 % P/L	No. Observations:	1834
Model:	ARDL(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10)	Log Likelihood	3495.555
Method:	Conditional MLE	S.D. of innovations	0.036
Date:	Wed, 17 Nov 2021	AIC	-6681.110
Time:	12:03:16	BIC	-5827.248
Sample:	10	HQIC	-6366.127
	1834

	Date	Close	Volume	RSI	RSI Condition	MACD Condition	EMA Condition	Close-EMA200 Price Diff	BB Condition	ATR	...	future_price3 P/L	future_price3 % P/L	future_price5 P/L	future_price5 % P/L	future_price10 P/L	future_price10 % P/L	future_price15 P/L	future_price15 % P/L	future_price30 P/L	future_price30 % P/L
199	19/7/2015	3683	1091997	44.534893	0.0	0	-1	-548.695000	0	1003.391727	...	-511.0	-0.138746	-501.0	-0.136030	-114.0	-0.030953	293.0	0.079555	-8.0	-0.002172
200	20/7/2015	3824	1092024	46.766872	0.0	0	-1	-403.638333	0	961.006604	...	-49.0	-0.012814	-264.0	-0.069038	-524.0	-0.137029	184.0	0.048117	-337.0	-0.088128
201	21/7/2015	3568	1092051	43.355566	0.0	0	-1	-653.074768	0	1049.648990	...	-386.0	-0.108184	-334.0	-0.093610	148.0	0.041480	218.0	0.061099	673.0	0.188621
202	22/7/2015	3172	1092072	38.658108	0.0	0	-1	-1038.636213	0	1051.388347	...	388.0	0.122320	461.0	0.145334	365.0	0.115069	486.0	0.153216	643.0	0.202711
203	23/7/2015	3775	1092113	47.912689	0.0	0	0	-431.301524	0	1068.646323	...	-541.0	-0.143311	-182.0	-0.048212	534.0	0.141457	-91.0	-0.024106	-192.0	-0.050861
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2748	11/7/2022	2424	1183685	44.525912	0.0	0	-1	-766.140365	0	1266.920532	...	1209.0	0.498762	689.0	0.284241	727.0	0.299917	734.0	0.302805	189.0	0.077970
2749	12/7/2022	2281	1183716	43.403383	0.0	0	-1	-900.094192	0	1298.569065	...	975.0	0.427444	173.0	0.075844	1021.0	0.447611	1551.0	0.679965	320.0	0.140289
2750	13/7/2022	2572	1183750	46.366588	0.0	0	-1	-603.033553	0	1317.099846	...	541.0	0.210342	14.0	0.005443	753.0	0.292768	7.0	0.002722	125.0	0.048600
2751	14/7/2022	3633	1183771	55.512316	0.0	1	1	453.409567	0	1311.949857	...	-1179.0	-0.324525	-1069.0	-0.294247	386.0	0.106248	-122.0	-0.033581	-163.0	-0.044867
2752	15/7/2022	3256	1183810	52.111912	0.0	0	1	75.649273	0	1295.096296	...	-670.0	-0.205774	327.0	0.100430	-380.0	-0.116708	-619.0	-0.190111	-95.0	-0.029177

	Index	Mean % Profit	positive
0	BTC Long	2.580380	True
1	BTC Short	-0.367261	False
2	BTC Hourly Long	12.344631	True
3	BTC Hourly Short	-0.628955	False

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	2.37	0.04	True
1	BTC Short	-0.42	0.04	False
2	DOGE Long	6.06	0.08	True
3	DOGE Short	3.80	0.05	True
4	Oil Long	2.16	0.02	True
5	Oil Short	1.63	0.02	True
6	Gold Long	1.30	0.01	True
7	Gold Short	0.85	0.01	True
8	SP500 Long	0.30	0.01	True
9	SP500 Short	0.68	0.01	True
10	STI Long	0.71	0.01	True
11	STI Short	0.73	0.01	True

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	3.36	0.07	True
1	BTC Short	-1.22	0.07	False
2	DOGE Long	8.64	0.15	True
3	DOGE Short	3.53	0.07	True
4	Oil Long	2.13	0.05	True
5	Oil Short	2.51	0.03	True
6	Gold Long	1.29	0.02	True
7	Gold Short	0.93	0.01	True
8	SP500 Long	-1.07	0.03	False
9	SP500 Short	0.37	0.01	True
10	STI Long	0.78	0.02	True
11	STI Short	0.84	0.02	True

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	4.07	0.09	True
1	BTC Short	-1.91	0.08	False
2	DOGE Long	10.98	0.12	True
3	DOGE Short	3.34	0.17	True
4	Oil Long	2.47	0.06	True
5	Oil Short	3.57	0.05	True
6	Gold Long	1.06	0.02	True
7	Gold Short	1.09	0.02	True
8	SP500 Long	-1.16	0.03	False
9	SP500 Short	0.48	0.02	True
10	STI Long	0.89	0.03	True
11	STI Short	1.26	0.02	True

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	7.00	0.14	True
1	BTC Short	-3.47	0.13	False
2	DOGE Long	10.89	0.22	True
3	DOGE Short	-0.29	0.20	False
4	Oil Long	2.63	0.07	True
5	Oil Short	4.72	0.06	True
6	Gold Long	0.57	0.02	True
7	Gold Short	0.77	0.03	True
8	SP500 Long	-1.38	0.06	False
9	SP500 Short	0.72	0.03	True
10	STI Long	1.12	0.03	True
11	STI Short	0.93	0.03	True

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	9.37	0.16	True
1	BTC Short	-5.18	0.15	False
2	DOGE Long	6.15	0.28	True
3	DOGE Short	-0.59	0.24	False
4	Oil Long	1.12	0.08	True
5	Oil Short	5.39	0.07	True
6	Gold Long	0.00	0.03	True
7	Gold Short	1.52	0.03	True
8	SP500 Long	-3.55	0.06	False
9	SP500 Short	0.65	0.03	True
10	STI Long	1.10	0.04	True
11	STI Short	0.29	0.04	True

	Index	Mean % Profit	Std % Profit	positive
0	BTC Long	14.21	0.29	True
1	BTC Short	-10.64	0.26	False
2	DOGE Long	-13.26	0.50	False
3	DOGE Short	-4.73	0.35	False
4	Oil Long	0.36	0.27	True
5	Oil Short	11.40	0.15	True
6	Gold Long	-1.01	0.04	False
7	Gold Short	2.06	0.04	True
8	SP500 Long	-2.18	0.08	False
9	SP500 Short	1.23	0.04	True
10	STI Long	1.12	0.06	True
11	STI Short	1.21	0.06	True