How to use Backtrader with CryptoDataDownload Data: Python

Jan. 3, 2024, 2:01 a.m.by CryptoDataDownload

Backtrader Background

Backtrader, a renowned Python library, stands out as an invaluable tool for testing trading strategies through backtesting. This powerful library allows you to simulate trading strategies using historical data, offering a critical insights into how the strategy performed previously and over different time horizons and volatility regimes. What sets this example apart is its unique integration with CryptoDataDownload, a leading source of free historical cryptocurrency data. By demonstrating how to use CryptoDataDownload cryptocurrency historical data into Backtrader, we anticipate new algorithmic strategy development and testing in the cryptocurrency space. This integration enables a comprehensive analysis of a variety (limitless) crypto trading strategies, making it an exemplary case study for those looking to delve into the increasingly significant world of cryptocurrency trading. This approach broadens the analytical capabilities of users, making it a foundation example in cryptocurrency strategy simulation and backtesting.

The Code

This code integrates historical cryptocurrency data from CryptoDataDownload with Backtrader to test a Simple Moving Average (SMA) Crossover trading strategy. It starts by fetching Bitcoin trading data from Binance, processes it using Pandas to fit the required format, and then feeds it into Backtrader. The SMACrossover class defines the trading strategy, triggering buys and sells based on the crossover of fast and slow moving averages. Key performance metrics, like Sharpe Ratio, annual return, and maximum drawdown, are calculated using Backtrader's analyzers, providing a factual assessment of the strategy's effectiveness based on past market data.

# CryptoDataDownload
# No Express Warranty
# MIT License
import pandas as pd
import backtrader as bt

class SMACrossover(bt.Strategy):
    # Define the two moving averages
    params = (
        ('fast_length', 10),  # period for the fast moving average
        ('slow_length', 30),  # period for the slow moving average
    )

def __init__(self):
        # Initialize moving averages
        self.sma_fast = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.fast_length)
        self.sma_slow = bt.indicators.SimpleMovingAverage(
            self.datas[0], period=self.params.slow_length)

# To track if the fast MA is above the slow MA
        self.crossover = bt.indicators.CrossOver(self.sma_fast, self.sma_slow)

def next(self):
        # Buy if the fast MA crosses above the slow MA
        if not self.position:  # not in the market
            if self.crossover > 0:  # if fast crosses above slow
                self.buy()

# Sell if the fast MA crosses below the slow MA
        elif self.crossover < 0:  # if fast crosses below slow
            self.close()  # close the position

def load_data(coin, exchange):
    """
    Function will load data for the data found at CryptoDataDownload.com
    """
    sym = coin.split("/")[0]    # get the symbol in front of the slash, ie BTC from BTC/USD
    sym_base = coin.split("/")[1]   # get the base symbol, ie USD in BTC/USD
    URL = f"https://www.cryptodatadownload.com/cdd/{exchange}_{sym+sym_base}_d.csv"   # path to file
    data = pd.read_csv(URL, skiprows=1)    # use pandas to read file from site
    data['Date'] = pd.to_datetime(data['Date'])
    data['datetime'] = data['Date'].apply(lambda x: int(x.timestamp() * 1000))
    data["datetime"] = data["datetime"].values.astype(dtype='datetime64[ms]')
    data.sort_values('datetime', ascending=True, inplace=True)  # we need to sort the data to have most recent data last in file
    data.set_index('datetime', inplace=True)
    data.drop(columns=[f'Volume {sym}', f'Volume {sym_base}', 'tradecount', 'Symbol', 'Unix', 'Date'], inplace=True) # drop columns we dont need
    return data  # return our large and combined dataframe

def main():
    COIN = "BTC/USDT"
    df = load_data(COIN, 'Binance')

# Convert the loaded DataFrame into a Backtrader feed and tell it which columns are named what
    data = bt.feeds.PandasData(dataname=df,
                               open='Open',
                               high='High',
                               low='Low',
                               close='Close',
                               volume=None,
                               openinterest=None
                               )

# Create a Cerebro engine instance for backtrader
    cerebro = bt.Cerebro()

# Add the data feed and add some analyzers for statistic info
    cerebro.adddata(data)
    cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='areturn')
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=3.95)
    cerebro.addanalyzer(bt.analyzers.Returns)
    cerebro.addanalyzer(bt.analyzers.DrawDown)

cerebro.addstrategy(SMACrossover)  # Add the strategy to Cerebro
    results = cerebro.run()     # Run the strategy

print(f"Sharpe: {results[0].analyzers.sharperatio.get_analysis()['sharperatio']:.3f}")
    print(f"Norm. Annual Return: {results[0].analyzers.returns.get_analysis()['rnorm100']:.2f}%")
    print(f"Max Drawdown: {results[0].analyzers.drawdown.get_analysis()['max']['drawdown']:.2f}%")

if __name__ == "__main__":
    main()

Notice: Information contained herein is not and should not be construed as an offer, solicitation, or recommendation to buy or sell securities. The information has been obtained from sources we believe to be reliable; however no guarantee is made or implied with respect to its accuracy, timeliness, or completeness. Author does not own the any crypto currency discussed. The information and content are subject to change without notice. CryptoDataDownload and its affiliates do not provide investment, tax, legal or accounting advice.

This material has been prepared for informational purposes only and is the opinion of the author, and is not intended to provide, and should not be relied on for, investment, tax, legal, accounting advice. You should consult your own investment, tax, legal and accounting advisors before engaging in any transaction. All content published by CryptoDataDownload is not an endorsement whatsoever. CryptoDataDownload was not compensated to submit this article. Please also visit our Privacy policy; disclaimer; and terms and conditions page for further information.

THE PERFORMANCE OF TRADING SYSTEMS IS BASED ON THE USE OF COMPUTERIZED SYSTEM LOGIC. IT IS HYPOTHETICAL. PLEASE NOTE THE FOLLOWING DISCLAIMER. CFTC RULE 4.41: HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN. U.S. GOVERNMENT REQUIRED DISCLAIMER: COMMODITY FUTURES TRADING COMMISSION. FUTURES AND OPTIONS TRADING HAS LARGE POTENTIAL REWARDS, BUT ALSO LARGE POTENTIAL RISK. YOU MUST BE AWARE OF THE RISKS AND BE WILLING TO ACCEPT THEM IN ORDER TO INVEST IN THE FUTURES AND OPTIONS MARKETS. DON’T TRADE WITH MONEY YOU CAN’T AFFORD TO LOSE. THIS IS NEITHER A SOLICITATION NOR AN OFFER TO BUY/SELL FUTURES OR OPTIONS. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE DISCUSSED ON THIS WEBSITE. THE PAST PERFORMANCE OF ANY TRADING SYSTEM OR METHODOLOGY IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.

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