breakout.py

# =============================================================================
# Backtesting strategy - II : Intraday resistance breakout strategy
# Author : Mayank Rasu

# Please report bug/issues in the Q&A section
# =============================================================================

import numpy as np
import pandas as pd
from alpha_vantage.timeseries import TimeSeries
import copy
import time


def ATR(DF,n):
    "function to calculate True Range and Average True Range"
    df = DF.copy()
    df['H-L']=abs(df['High']-df['Low'])
    df['H-PC']=abs(df['High']-df['Close'].shift(1))
    df['L-PC']=abs(df['Low']-df['Close'].shift(1))
    df['TR']=df[['H-L','H-PC','L-PC']].max(axis=1,skipna=False)
    df['ATR'] = df['TR'].rolling(n).mean()
    #df['ATR'] = df['TR'].ewm(span=n,adjust=False,min_periods=n).mean()
    df2 = df.drop(['H-L','H-PC','L-PC'],axis=1)
    return df2['ATR']

def CAGR(DF):
    "function to calculate the Cumulative Annual Growth Rate of a trading strategy"
    df = DF.copy()
    df["cum_return"] = (1 + df["ret"]).cumprod()
    n = len(df)/(252*78)
    CAGR = (df["cum_return"].tolist()[-1])**(1/n) - 1
    return CAGR

def volatility(DF):
    "function to calculate annualized volatility of a trading strategy"
    df = DF.copy()
    vol = df["ret"].std() * np.sqrt(252*78)
    return vol

def sharpe(DF,rf):
    "function to calculate sharpe ratio ; rf is the risk free rate"
    df = DF.copy()
    sr = (CAGR(df) - rf)/volatility(df)
    return sr
    

def max_dd(DF):
    "function to calculate max drawdown"
    df = DF.copy()
    df["cum_return"] = (1 + df["ret"]).cumprod()
    df["cum_roll_max"] = df["cum_return"].cummax()
    df["drawdown"] = df["cum_roll_max"] - df["cum_return"]
    df["drawdown_pct"] = df["drawdown"]/df["cum_roll_max"]
    max_dd = df["drawdown_pct"].max()
    return max_dd

# Download historical data (monthly) for selected stocks

tickers = ["MSFT","AAPL","FB","AMZN","INTC", "CSCO","VZ","IBM","TSLA","AMD"]
         
key_path = "D:\\Udemy\\Quantitative Investing Using Python\\1_Getting Data\\AlphaVantage\\key.txt"
ts = TimeSeries(key=open(key_path,'r').read(), output_format='pandas')

ohlc_intraday = {} # directory with ohlc value for each stock   
api_call_count = 1
ts = TimeSeries(key=open(key_path,'r').read(), output_format='pandas')
start_time = time.time()
for ticker in tickers:
    data = ts.get_intraday(symbol=ticker,interval='5min', outputsize='full')[0]
    api_call_count+=1
    data.columns = ["Open","High","Low","Close","Volume"]
    data = data.iloc[::-1]
    data = data.between_time('09:35', '16:00') #remove data outside regular trading hours
    ohlc_intraday[ticker] = data
    if api_call_count==5:
        api_call_count = 1
        time.sleep(60 - ((time.time() - start_time) % 60.0))

tickers = ohlc_intraday.keys() # redefine tickers variable after removing any tickers with corrupted data

################################Backtesting####################################

# calculating ATR and rolling max price for each stock and consolidating this info by stock in a separate dataframe
ohlc_dict = copy.deepcopy(ohlc_intraday)
tickers_signal = {}
tickers_ret = {}
for ticker in tickers:
    print("calculating ATR and rolling max price for ",ticker)
    ohlc_dict[ticker]["ATR"] = ATR(ohlc_dict[ticker],20)
    ohlc_dict[ticker]["roll_max_cp"] = ohlc_dict[ticker]["High"].rolling(20).max()
    ohlc_dict[ticker]["roll_min_cp"] = ohlc_dict[ticker]["Low"].rolling(20).min()
    ohlc_dict[ticker]["roll_max_vol"] = ohlc_dict[ticker]["Volume"].rolling(20).max()
    ohlc_dict[ticker].dropna(inplace=True)
    tickers_signal[ticker] = ""
    tickers_ret[ticker] = [0]


# identifying signals and calculating daily return (stop loss factored in)
for ticker in tickers:
    print("calculating returns for ",ticker)
    for i in range(1,len(ohlc_dict[ticker])):
        if tickers_signal[ticker] == "":
            tickers_ret[ticker].append(0)
            if ohlc_dict[ticker]["High"][i]>=ohlc_dict[ticker]["roll_max_cp"][i] and \
               ohlc_dict[ticker]["Volume"][i]>1.5*ohlc_dict[ticker]["roll_max_vol"][i-1]:
                tickers_signal[ticker] = "Buy"
            elif ohlc_dict[ticker]["Low"][i]<=ohlc_dict[ticker]["roll_min_cp"][i] and \
               ohlc_dict[ticker]["Volume"][i]>1.5*ohlc_dict[ticker]["roll_max_vol"][i-1]:
                tickers_signal[ticker] = "Sell"
        
        elif tickers_signal[ticker] == "Buy":
            if ohlc_dict[ticker]["Low"][i]<ohlc_dict[ticker]["Close"][i-1] - ohlc_dict[ticker]["ATR"][i-1]:
                tickers_signal[ticker] = ""
                tickers_ret[ticker].append(((ohlc_dict[ticker]["Close"][i-1] - ohlc_dict[ticker]["ATR"][i-1])/ohlc_dict[ticker]["Close"][i-1])-1)
            elif ohlc_dict[ticker]["Low"][i]<=ohlc_dict[ticker]["roll_min_cp"][i] and \
               ohlc_dict[ticker]["Volume"][i]>1.5*ohlc_dict[ticker]["roll_max_vol"][i-1]:
                tickers_signal[ticker] = "Sell"
                tickers_ret[ticker].append((ohlc_dict[ticker]["Close"][i]/ohlc_dict[ticker]["Close"][i-1])-1)
            else:
                tickers_ret[ticker].append((ohlc_dict[ticker]["Close"][i]/ohlc_dict[ticker]["Close"][i-1])-1)
                
        elif tickers_signal[ticker] == "Sell":
            if ohlc_dict[ticker]["High"][i]>ohlc_dict[ticker]["Close"][i-1] + ohlc_dict[ticker]["ATR"][i-1]:
                tickers_signal[ticker] = ""
                tickers_ret[ticker].append((ohlc_dict[ticker]["Close"][i-1]/(ohlc_dict[ticker]["Close"][i-1] + ohlc_dict[ticker]["ATR"][i-1]))-1)
            elif ohlc_dict[ticker]["High"][i]>=ohlc_dict[ticker]["roll_max_cp"][i] and \
               ohlc_dict[ticker]["Volume"][i]>1.5*ohlc_dict[ticker]["roll_max_vol"][i-1]:
                tickers_signal[ticker] = "Buy"
                tickers_ret[ticker].append((ohlc_dict[ticker]["Close"][i-1]/ohlc_dict[ticker]["Close"][i])-1)
            else:
                tickers_ret[ticker].append((ohlc_dict[ticker]["Close"][i-1]/ohlc_dict[ticker]["Close"][i])-1)
                
    ohlc_dict[ticker]["ret"] = np.array(tickers_ret[ticker])


# calculating overall strategy's KPIs
strategy_df = pd.DataFrame()
for ticker in tickers:
    strategy_df[ticker] = ohlc_dict[ticker]["ret"]
strategy_df["ret"] = strategy_df.mean(axis=1)
CAGR(strategy_df)
sharpe(strategy_df,0.025)
max_dd(strategy_df)  


# vizualization of strategy return
(1+strategy_df["ret"]).cumprod().plot()


#calculating individual stock's KPIs
cagr = {}
sharpe_ratios = {}
max_drawdown = {}
for ticker in tickers:
    print("calculating KPIs for ",ticker)      
    cagr[ticker] =  CAGR(ohlc_dict[ticker])
    sharpe_ratios[ticker] =  sharpe(ohlc_dict[ticker],0.025)
    max_drawdown[ticker] =  max_dd(ohlc_dict[ticker])

KPI_df = pd.DataFrame([cagr,sharpe_ratios,max_drawdown],index=["Return","Sharpe Ratio","Max Drawdown"])      
KPI_df.T