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Contrarian_Countdown_Cross.py

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+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         =   8
+ma_lookback      =   3
+cross_ma_lookback = 13
+upper_barrier    =  10
+lower_barrier    = -10
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def signal(Data, rsi_col, ma_col, buy, sell):
+
+    Data = adder(Data, 10)
+    Data = rounding(Data, 5)
+
+    for i in range(len(Data)):
+
+        if Data[i, rsi_col] > Data[i, ma_col] and Data[i - 1, rsi_col] < Data[i - 1, ma_col]:
+
+            Data[i, buy] = 1
+
+        elif Data[i, rsi_col] < Data[i, ma_col] and Data[i - 1, rsi_col] > Data[i - 1, ma_col]:
+
+            Data[i, sell] = -1    
+    return Data
+
+##############################################################################   1
+
+my_data = countdown_indicator(my_data, lookback, ma_lookback, 0, 1, 2, 3, 4)
+my_data = ma(my_data, cross_ma_lookback, 4, 5)
+my_data = signal(my_data, 4, 5, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)

Contrarian_Countdown_Extremes.py

@@ -0,0 +1,54 @@
+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         =   8
+ma_lookback      =   3
+upper_barrier    =  10
+lower_barrier    = -10
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def signal(Data, what, buy, sell):
+
+    Data = adder(Data, 10)
+    Data = rounding(Data, 5)
+
+    for i in range(len(Data)):
+
+        if Data[i, what] <= lower_barrier and Data[i - 1, buy] == 0 and \
+            Data[i - 2, buy] == 0 and Data[i - 3, buy] == 0 and Data[i - 4, buy] == 0:
+            Data[i, buy] = 1
+
+        elif Data[i, what] >= upper_barrier and Data[i - 1, sell] == 0 and \
+            Data[i - 2, sell] == 0 and Data[i - 3, sell] == 0 and Data[i - 4, sell] == 0:
+            Data[i, sell] = -1    
+
+    return Data    
+
+##############################################################################   1
+
+my_data = countdown_indicator(my_data, lookback, ma_lookback, 0, 1, 2, 3, 4)
+my_data = signal(my_data, 4, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)

Contrarian_Demarker_Cross.py

@@ -0,0 +1,54 @@
+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         = 14
+upper_barrier    = 0.70
+lower_barrier    = 0.30
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def signal(Data, rsi_col, ma_col, buy, sell):
+
+    Data = adder(Data, 10)
+    Data = rounding(Data, 5)
+
+    for i in range(len(Data)):
+
+        if Data[i, rsi_col] > Data[i, ma_col] and Data[i - 1, rsi_col] < Data[i - 1, ma_col]:
+
+            Data[i, buy] = 1
+
+        elif Data[i, rsi_col] < Data[i, ma_col] and Data[i - 1, rsi_col] > Data[i - 1, ma_col]:
+
+            Data[i, sell] = -1    
+    return Data
+
+
+##############################################################################   1
+
+my_data = demarker(my_data, lookback, 1, 2, 4)
+my_data = ma(my_data, ma_lookback, 4, 5)
+my_data = signal(my_data, 4, 5, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)

Contrarian_Demarker_Divergences.py

@@ -0,0 +1,105 @@
+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         = 14
+upper_barrier    = 0.70
+lower_barrier    = 0.30
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def divergence(Data, indicator, lower_barrier, upper_barrier, width, buy, sell):
+
+    Data = adder(Data, 10)
+
+    for i in range(len(Data)):
+
+        try:
+            if Data[i, indicator] < lower_barrier:
+
+                for a in range(i + 1, i + width):
+
+                    # First trough
+                    if Data[a, indicator] > lower_barrier:
+
+                        for r in range(a + 1, a + width):
+
+                            if Data[r, indicator] < lower_barrier and \
+                            Data[r, indicator] > Data[i, indicator] and Data[r, 3] < Data[i, 3]:
+
+                                for s in range(r + 1, r + width):
+
+                                    # Second trough
+                                    if Data[s, indicator] > lower_barrier:
+                                        Data[s, buy] = 1
+                                        break
+
+                                    else:
+                                        break
+                            else:
+                                break
+                        else:
+                            break
+                    else:
+                        break
+
+        except IndexError:
+            pass
+
+    for i in range(len(Data)):
+
+        try:
+            if Data[i, indicator] > upper_barrier:
+
+                for a in range(i + 1, i + width):
+
+                    # First trough
+                    if Data[a, indicator] < upper_barrier:
+                        for r in range(a + 1, a + width):
+                            if Data[r, indicator] > upper_barrier and \
+                            Data[r, indicator] < Data[i, indicator] and Data[r, 3] > Data[i, 3]:
+                                for s in range(r + 1, r + width):
+
+                                    # Second trough
+                                    if Data[s, indicator] < upper_barrier:
+                                        Data[s, sell] = -1
+                                        break
+                                    else:
+                                        break
+                            else:
+                                break
+                        else:
+                            break
+                    else:
+                        break
+        except IndexError:
+            pass 
+    return Data
+
+
+##############################################################################   1
+
+my_data = demarker(my_data, lookback, 1, 2, 4)
+my_data = divergence(my_data, 4, lower_barrier, upper_barrier, width, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)

Contrarian_Demarker_Extremes.py

@@ -0,0 +1,53 @@
+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         = 14
+upper_barrier    = 0.70
+lower_barrier    = 0.30
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def signal(Data, what, buy, sell):
+
+    Data = adder(Data, 10)
+    Data = rounding(Data, 5)
+
+    for i in range(len(Data)):
+
+        if Data[i, what] <= lower_barrier and Data[i - 1, buy] == 0 and \
+            Data[i - 2, buy] == 0 and Data[i - 3, buy] == 0 and Data[i - 4, buy] == 0:
+            Data[i, buy] = 1
+
+        elif Data[i, what] >= upper_barrier and Data[i - 1, sell] == 0 and \
+            Data[i - 2, sell] == 0 and Data[i - 3, sell] == 0 and Data[i - 4, sell] == 0:
+            Data[i, sell] = -1    
+
+    return Data  
+
+##############################################################################   1
+
+my_data = demarker(my_data, lookback, 1, 2, 4)
+my_data = signal(my_data, 4, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)

Contrarian_Fisher_Extremes.py

@@ -0,0 +1,53 @@
+
+# Base parameters
+expected_cost    = 0.0 * (lot / 100000) 
+assets           = asset_list(1) 
+window           = 1000
+
+# Trading parameters   
+horizon          = 'H1'
+
+# Indicator / Strategy parameters
+lookback         = 14
+upper_barrier    =  2
+lower_barrier    = -2
+
+# Mass imports 
+my_data = mass_import(0, horizon)
+
+def signal(Data, what, buy, sell):
+
+    Data = adder(Data, 10)
+    Data = rounding(Data, 5)
+
+    for i in range(len(Data)):
+
+        if Data[i, what] <= lower_barrier and Data[i - 1, buy] == 0 and \
+            Data[i - 2, buy] == 0 and Data[i - 3, buy] == 0 and Data[i - 4, buy] == 0:
+            Data[i, buy] = 1
+
+        elif Data[i, what] >= upper_barrier and Data[i - 1, sell] == 0 and \
+            Data[i - 2, sell] == 0 and Data[i - 3, sell] == 0 and Data[i - 4, sell] == 0:
+            Data[i, sell] = -1    
+
+    return Data  
+
+##############################################################################   1
+
+my_data = fisher_transform(my_data, lookback, 3, 4)
+my_data = signal(my_data, 4, 6, 7)
+
+holding(my_data, 6, 7, 8, 9)
+my_data_eq = equity_curve(my_data, 8, expected_cost, lot, investment)
+performance(my_data_eq, 8, my_data, assets[0])
+
+if sigchart == True:
+    signal_chart_ohlc_color(my_data, assets[0], 3, 6, 7, window = 500)
+    indicator_plot_double(my_data, 0, 1, 2, 3, 4, window = 250)
+    plt.axhline(y = upper_barrier, color = 'black', linewidth = 1, linestyle = '--')
+    plt.axhline(y = lower_barrier, color = 'black', linewidth = 1, linestyle = '--')    
+
+plt.plot(my_data_eq[:, 3], linewidth = 1, label = assets[0])
+plt.grid()
+plt.legend()
+plt.axhline(y = investment, color = 'black', linewidth = 1)