Implementing Common Trading Strategies

Note: This tutorial assumes that you have basic knowledge of Python programming and have set up a Python environment with the necessary libraries installed (pandas, numpy, requests, ccxt, etc.).

Introduction

In this tutorial, we will cover the implementation of three common trading strategies in Python:

Moving average crossover
MACD and signal line crossover
Bollinger Bands

Fetching market data

First, let’s fetch some historical price data from a cryptocurrency exchange. We will use the ccxt library to do this. Make sure to install the library using pip install ccxt if you haven’t already.

import ccxt
import pandas as pd

exchange = ccxt.binance()
symbol = 'BTC/USDT'
timeframe = '1d'
limit = 500

ohlcv_data = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
df = pd.DataFrame(ohlcv_data, columns=columns)
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df.set_index('timestamp', inplace=True)

Strategy 1: Moving average crossover

The moving average crossover strategy generates a buy signal when a shorter-term moving average crosses above a longer-term moving average and a sell signal when the shorter-term moving average crosses below the longer-term moving average.

3.1. Calculating moving averages

short_window = 50
long_window = 200

df['short_mavg'] = df['close'].rolling(window=short_window).mean()
df['long_mavg'] = df['close'].rolling(window=long_window).mean()

3.2. Generating buy and sell signals

df['signal'] = 0
df.loc[df['short_mavg'] > df['long_mavg'], 'signal'] = 1
df.loc[df['short_mavg'] < df['long_mavg'], 'signal'] = -1

Strategy 2: MACD and signal line crossover

The MACD (Moving Average Convergence Divergence) indicator is calculated as the difference between two exponential moving averages (EMA) of closing prices. A signal line, which is an EMA of the MACD, is used to generate trading signals.

4.1. Calculating MACD and signal line

fast_length = 12
slow_length = 26
signal_length = 9

df['fast_ema'] = df['close'].ewm(span=fast_length).mean()
df['slow_ema'] = df['close'].ewm(span=slow_length).mean()
df['macd'] = df['fast_ema'] - df['slow_ema']
df['signal_line'] = df['macd'].ewm(span=signal_length).mean()

4.2. Generating buy and sell signals

df['macd_signal'] = 0
df.loc[df['macd'] > df['signal_line'], 'macd_signal'] = 1
df.loc[df['macd'] < df['signal_line'], 'macd_signal'] = -1

Strategy 3: Bollinger Bands

Bollinger Bands are a volatility indicator consisting of a simple moving average (SMA) and two standard deviation bands above and below the SMA. A common strategy is to buy when the price touches the lower Bollinger Band and sell when it touches the upper Bollinger Band.

5.1. Calculating Bollinger Bands

sma_length = 20
std_dev_multiplier = 2

df['sma'] = df['close'].rolling(window=sma_length).mean()
df['std_dev'] = df['close'].rolling(window=sma_length).std()
df['upper_band'] = df['sma'] + (df['std_dev'] * std_dev_multiplier)
df['lower_band'] = df['sma'] - (df['std_dev'] * std_dev_multiplier)

5.2. Generating buy and sell signals

df['bollinger_signal'] = 0
df.loc[df['close'] < df['lower_band'], 'bollinger_signal'] = 1
df.loc[df['close'] > df['upper_band'], 'bollinger_signal'] = -1

Visualizing the trading signals

To better understand the trading signals generated by each strategy, we can create a simple plot using matplotlib.

import matplotlib.pyplot as plt

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 18), sharex=True)

# Moving average crossover
ax1.plot(df.index, df['close'], label='Close', alpha=0.5)
ax1.plot(df.index, df['short_mavg'], label=f'Short MA ({short_window})', linestyle='--')
ax1.plot(df.index, df['long_mavg'], label=f'Long MA ({long_window})', linestyle='--')
ax1.legend(loc='best')
ax1.set_title('Moving Average Crossover Strategy')

# MACD and signal line crossover
ax2.plot(df.index, df['macd'], label='MACD', alpha=0.5)
ax2.plot(df.index, df['signal_line'], label='Signal Line', linestyle='--')
ax2.legend(loc='best')
ax2.set_title('MACD and Signal Line Crossover Strategy')

# Bollinger Bands
ax3.plot(df.index, df['close'], label='Close', alpha=0.5)
ax3.plot(df.index, df['sma'], label=f'SMA ({sma_length})', linestyle='--')
ax3.plot(df.index, df['upper_band'], label='Upper Band', linestyle='--')
ax3.plot(df.index, df['lower_band'], label='Lower Band', linestyle='--')
ax3.legend(loc='best')
ax3.set_title('Bollinger Bands Strategy')

plt.show()

Conclusion

In this tutorial, we have implemented three common trading strategies in Python: moving average crossover, MACD and signal line crossover, and Bollinger Bands. These strategies are just the tip of the iceberg when it comes to algorithmic trading. You can experiment with different parameters, indicators, and strategies to find what works best for your specific trading goals. Always remember to backtest your strategies with historical data and manage risk appropriately.