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Author: PyPatel

PCR_strategy.py

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+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+import math
+import quandl
+
+def variance_calculator(series, series_average, win_len):
+    sma = win_len
+    temp = series.subtract(series_average) # Difference a-b
+    temp2 = temp.apply(lambda x:x**2)  # Square them.. (a-b)^2
+    temp3 = temp2.rolling(sma - 1).mean() # Summation (a-b)^2 / (sma - 1)
+    sigma = temp3.apply(lambda x: math.sqrt(x)) 
+    return sigma
+
+def fetch_data(string1, string2, string3, filename):
+    w = quandl.get(string1, authtoken = string2, start_date = string3)
+    w.to_csv(filename)
+    w = pd.read_csv(filename)
+    return w
+
+# The Data pulled from quandl and stored locally for faster execution
+Data = fetch_data("CHRIS/CME_SP1", "", "2017-07-31", "local_future.csv")
+Data1 = fetch_data("CHRIS/SPX_PC", "", "2017-07-31", "local_data.csv")
+
+Data['future'] = Data1['Last']
+Data['PCR'] = Data['S&P PUT-CALL RATIO']
+
+# Variables
+
+sma = 20 #  Moving Average Window length
+k = 1.5 # Constant representing 'k' times sigma away from moving average (fir Bollinger bands)
+l = 2 # cons.representing 'l' times sigma away from Bollinger band (for stoploss band) 
+pro = 0  # Profit Variable
+flag = 1 # Flag is there for begin first transaction -- transaction should start with LBB/UBB crossing only
+buy_flag = False
+sell_flag = False
+transaction_start_price = 0
+abs_SL = 25  # Absolute Stoploss Variable
+mtm = list()
+order_details = list()
+order = list()  #list which contains the orders: BUY / SELL / Do Nothing
+profit = list()
+buy_sell = list()
+stoploss = list()
+trade_cause = list()
+
+Data['mAvg'] = Data['PCR'].rolling(sma).mean() # Calculating the moving average of the PCR
+Data['PCR_prev'] = Data['PCR'].shift(1)
+
+sigma = variance_calculator(Data['PCR'], Data['mAvg'], sma )  # Calculating the standard deviation
+k_sigma = k * sigma
+l_sigma = l * sigma
+
+Data['UBB'] = Data['mAvg'].add(k_sigma) # Upper Bollinger Band
+Data['LBB'] = Data['mAvg'].subtract(k_sigma)  # Lower Bollinger Band
+Data['USL'] = Data['UBB'].add(l_sigma)  # Upper Stoploss Band
+Data['LSL'] = Data['LBB'].subtract(l_sigma)  # Lower Stoploss Band
+Data['order'] = pd.Series() # list which contains the orders: BUY / SELL / Do Nothing
+
+s = Data['PCR'].size  # Total Number of Data point
+
+for i in range(s):
+    
+    pro = 0    # Profit at each trade
+    future_cost = Data['future'][i]   # Cost of big S&P 500 futures bought
+    PCR = Data['PCR'][i] # Put Call ratio 
+    PCR_prev = Data['PCR_prev'][i]  # Previous day's put call ratio
+    LBB = Data['LBB'][i]  # Lower Bollinger Band
+    UBB = Data['UBB'][i]  # # Upper Bollinger Band
+    mAvg = Data['mAvg'][i] # Moving Average
+    USL = Data['USL'][i]  # Upper Stoploss Band
+    LSL = Data['LSL'][i]  # Lower Stoploss Band
+
+    # Comparisons stores as boolean variables a to place order accordingly
+    UBB_cross = (PCR > UBB) and (PCR_prev < UBB)  # Check if PCR crosses Upper Bollinger Band
+    LBB_cross = (PCR < LBB) and (PCR_prev > LBB)  # Check if PCR crosses Lower Bollinger Band
+    mAvg_cross_up = (PCR > mAvg) and (PCR_prev < mAvg)  # Check if PCR crosses moving average from low to high
+    mAvg_cross_down = (PCR < mAvg) and (PCR_prev > mAvg)  # Check if PCR crosses moving average from high to low
+    USL_cross = (PCR > USL) and (PCR_prev < USL) # Check if PCR Crosses upper stoploss band
+    LSL_cross = (PCR < LSL) and (PCR_prev > LSL) # Check if PCR Crosses lower stoploss band
+
+
+    # Strategy
+    if(UBB_cross and (not buy_flag) and flag == 1):  # Places "BUY" order if PCR crosses upper Bollinger band to open trade
+        flag = 0
+        buy_flag = True
+        sell_flag = False
+        transaction_start_price = future_cost  # Price at which s&P 500 future bought when the order is placed
+        order_details = [1, "Buy", "UBB Crossed", "0", "Position taken"]
+
+    elif(LBB_cross and (not sell_flag) and flag == 1):
+        flag = 0
+        sell_flag = True
+        buy_flag = False
+        transaction_start_price = future_cost
+        order_details = [-1, "Sell", "LBB Crossed", "0", "Position taken"]
+
+     elif(mAvg_cross_up and (not buy_flag) and flag == 0):  # Places "BUY" order if PCR crosses mAvg from low to high to close a trade
+        flag = 1
+        buy_flag = False
+        sell_flag = False
+        pro = future_cost - transaction_start_price
+        order_details = [1, "Buy", "mAvg Crossed", "0", "Position Closed"]
+
+    elif(LSL_cross and (not buy_flag) and flag == 0):  # Places "BUY" order if PCR crosses lower stoploss band to close a trade
+        flag = 1
+        buy_flag = False
+        sell_flag = False
+        pro = future_cost - transaction_start_price
+        order_details = [1, "Buy", "LSB Crossed", "Stoploss Executed", "Position Closed"]
+
+    elif((future_cost - transaction_start_price) > abs_SL and (not buy_flag) and flag == 0):  # Places "BUY" order if PCR crosses lower stoploss absolute value
+        flag = 0
+        buy_flag = False
+        sell_flag = False
+        pro = future_cost - transaction_start_price
+        order_details = [1, "Buy", "LSB Crossed", "Stoploss Executed", "Position Closed"]
+
+     elif(mAvg_cross_down and (not sell_flag) and flag == 0):  # Places "Sell" order if PCR crosses mAvg from high to low to close a trade
+        flag = 1
+        buy_flag = False
+        sell_flag = False
+        pro = - (future_cost - transaction_start_price)
+        order_details = [-1, "Sell", "mAvg Crossed (H to L)", "0", "Position Closed"]
+
+    elif(USL_cross and (not sell_flag) and flag == 0):  # Places "Sell" order if PCR crosses Upper stoploss band to close a trade
+        flag = 1
+        buy_flag = False
+        sell_flag = False
+        pro = - (future_cost - transaction_start_price)
+        order_details = [-1, "Sell", "USB Crossed", "Stoploss Executed", "Position Closed"]
+
+    elif( (- future_cost - transaction_start_price) > abs_SL and (not sell_flag) and flag == 0):  # Places "SELL" order if PCR crosses Upper stoploss absolute value
+        flag = 1
+        buy_flag = False
+        sell_flag = False
+        pro = - (future_cost - transaction_start_price)
+        order_details = [-1, "Sell", "USB Crossed", "Abs Stoploss Executed", "Position Closed"]
+
+    else:
+        if(buy_flag == 0 and sell_flag == 0):
+            tempo = "0"
+        else:
+            if(buy_flag == 1 and sell_flag == 0):
+                tempo = (Data['future'][i] - transaction_start_price) * 500
+            if(buy_flag == 0 and sell_flag == 1):
+                tempo = (- Data['future'][i] + transaction_start_price) * 500
+        order_details = [0, "No Trade", "No Trade", "0", tempo]
+
+    profit.append(pro)
+    order.append(order_details[0])
+    buy_sell.append(order_details[1])
+    trade_cause.append(order_details[2])
+    stoploss.append(order_details[3])
+    mtm.append(order_details[4])
+
+Data['placed_order'] = pd.Series(order)  # Converting list into Panda Series
+Data['cost'] = - (Data['placed_order'].multiply(Data['future'])) * 500  #  Cost of each transaction
+Data['out'] = Data['cost'].cumsum()   # Out is the cumulative cost profit / loss after transaction till now
+Data['buy_sell'] = pd.Series(buy_sell)
+Data['profit'] = -pd.Series(profit) * 500
+Data['stoploss'] = pd.Series(stoploss)
+Data['trade_cause'] = pd.Series(trade_cause)
+Data['mtm'] = pd.Series(mtm)
+
+print(Data['out'])
+
+output = pd.DataFrame() # Final output to be stored in excel file
+output['date'] = Data['Date']
+output['Close'] = Data['future']
+output['PCR'] = Data['PCR']
+output['placed_order'] = Data['placed_order']
+output['buy_sell'] = Data['buy_sell']
+output['trade_cause'] = Data['trade_cause']
+output['PnL'] = Data['profit']
+output['mtm'] = Data['mtm']
+output['stoploss'] = Data['stoploss']
+output['Cash Account'] = Data['out']
+
+output.to_excel('PCR_SL_output.xlsx', sheet_name='Sheet1')
+
+
+# Plot
+plt.plot(Data['PCR'])
+plt.plot(Data['mAvg'])
+plt.plot(Data['UBB'])
+plt.plot(Data['LBB'])
+plt.show()