BBBacktester.py

import os
import numpy as np
import pandas as pd
import seaborn as sns
from oandapyV20 import API
from dotenv import load_dotenv
import matplotlib.pyplot as plt
from scipy.optimize import brute
import oandapyV20.endpoints.instruments as instruments

load_dotenv()
oanda_api_key = os.getenv('OANDA_API_KEY')

class BBBacktester:
    ''' Class for the vectorized backtesting of Mean Reversion-based trading strategies (Bollinger Bands).

    Attributes
    ==========
    symbol: str
        ticker symbol with which to work with
    SMA: int
        time window for SMA
    dev: int
        distance for Lower/Upper Bands in Standard Deviation units
    start: str
        start date for data retrieval
    end: str
        end date for data retrieval
    tc: float
        proportional transaction costs per trade
        
    Methods
    =======
    get_data:
        retrieves and prepares the data
        
    set_parameters:
        sets one or two new parameters for SMA and dev
        
    test_strategy:
        runs the backtest for the Mean Reversion-based strategy
        
    plot_results:
        plots the performance of the strategy compared to buy and hold
        
    update_and_run:
        updates parameters and returns the negative absolute performance (for minimization algorithm)
        
    optimize_parameters:
        implements a brute force optimization for the two parameters
    '''
    
    def __init__(self, symbol, SMA, dev, start, end, granularity, tc = 0.0):
        self.symbol = symbol
        self.SMA = SMA
        self.dev = dev
        self.start = start
        self.end = end
        self.granularity = granularity
        self.tc = tc
        self.results = None
        self.data = None
        self.get_data()
        
    def __repr__(self):
        rep = "BBBacktester(symbol = {}, SMA = {}, dev = {}, start = {}, end = {})"
        return rep.format(self.symbol, self.SMA, self.dev, self.start, self.end)
        
    def get_data(self):
        ''' Retrieves and prepares the data from Oanda.
        '''

        client = API(access_token= oanda_api_key)

        # Define the request parameters
        params = {
            "from": self.start,
            "to": self.end,
            "granularity": self.granularity,  # Daily granularity, adjust as needed
        }
        
        # Fetch historical forex data from OANDA
        request = instruments.InstrumentsCandles(instrument=self.symbol, params=params)
        client.request(request)
        response = request.response

        # Isolate candlestick data from API response
        candles = response['candles']
        data_list = []

        for candle in candles:
            time = pd.to_datetime(candle['time'])
            open_price = float(candle['mid']['o'])
            high_price = float(candle['mid']['h'])
            low_price = float(candle['mid']['l'])
            close_price = float(candle['mid']['c'])
            volume = int(candle['volume'])

            data_list.append([time, open_price, high_price, low_price, close_price, volume])

        # Create a pandas DataFrame
        columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
        data = pd.DataFrame(data_list, columns=columns)

        # Set the 'Date' column as the index
        data.set_index('Date', inplace=True)
    
        data["returns"] = np.log(data["Close"] / data["Close"].shift(1))
        data["SMA"] = data["Close"].rolling(self.SMA).mean()
        data["Lower"] = data["SMA"] - data["Close"].rolling(self.SMA).std() * self.dev
        data["Upper"] = data["SMA"] + data["Close"].rolling(self.SMA).std() * self.dev
        
        self.data = data

        
    def set_parameters(self, SMA = None, dev = None):
        ''' Updates parameters and resp. time series.
        '''
        if SMA is not None:
            self.SMA = SMA
            self.data["SMA"] = self.data["Close"].rolling(self.SMA).mean()
            self.data["Lower"] = self.data["SMA"] - self.data["Close"].rolling(self.SMA).std() * self.dev
            self.data["Upper"] = self.data["SMA"] + self.data["Close"].rolling(self.SMA).std() * self.dev
            
        if dev is not None:
            self.dev = dev
            self.data["Lower"] = self.data["SMA"] - self.data["Close"].rolling(self.SMA).std() * self.dev
            self.data["Upper"] = self.data["SMA"] + self.data["Close"].rolling(self.SMA).std() * self.dev
            
    def test_strategy(self):
        ''' Backtests the trading strategy.
        '''
        data = self.data.copy().dropna()
        data["distance"] = data["Close"] - data.SMA
        data["position"] = np.where(data["Close"] < data.Lower, 1, np.nan)
        data["position"] = np.where(data["Close"] > data.Upper, -1, data["position"])
        data["position"] = np.where(data.distance * data.distance.shift(1) < 0, 0, data["position"])
        data["position"] = data.position.ffill().fillna(0)
        data["strategy"] = data.position.shift(1) * data["returns"]
        data.dropna(inplace = True)
        
        # determine when a trade takes place
        data["trades"] = data.position.diff().fillna(0).abs()
        
        # subtract transaction costs from return when trade takes place
        data.strategy = data.strategy - data.trades * self.tc
        
        data["creturns"] = data["returns"].cumsum().apply(np.exp)
        data["cstrategy"] = data["strategy"].cumsum().apply(np.exp)
        
        self.results = data
       
        # absolute performance of the strategy
        perf = data["cstrategy"].iloc[-1]
        # out-/underperformance of strategy
        outperf = perf - data["creturns"].iloc[-1]
        
        
        return [perf, outperf, self.results]
    
    def plot_results(self):
        ''' Plots the cumulative performance of the trading strategy
        compared to buy and hold.
        '''
        if self.results is None:
            print("No results to plot yet. Run a strategy.")
        else:
            title = "{} | SMA = {} | dev = {} | TC = {}".format(self.symbol, self.SMA, self.dev, self.tc)
            self.results[["creturns", "cstrategy"]].plot(title=title, figsize=(12, 8))
        
    def update_and_run(self, boll):
        ''' Updates parameters and returns the negative absolute performance (for minimization algorithm).

        Parameters
        ==========
        Params: tuple
            parameter tuple with SMA and dist
        '''
        self.set_parameters(int(boll[0]), int(boll[1]))
        return -self.test_strategy()[0]
    
    def optimize_parameters(self, SMA_range, dev_range):
        ''' Finds global maximum given the parameter ranges.

        Parameters
        ==========
        SMA_range, dist_range: tuple
            tuples of the form (start, end, step size)
        '''
        opt = brute(self.update_and_run, (SMA_range, dev_range), finish=None)
        return opt, -self.update_and_run(opt)