The foreign exchange (forex) market, known for its high liquidity and 24-hour trading cycle, attracts a large number of traders. Quantitative trading strategies are also very popular in the forex market. Below are three classic forex trading strategies that can be implemented quantitatively.

1. Momentum Trading Strategy

The momentum strategy is based on the assumption that market prices tend to continue moving in the same direction for a period of time. Momentum traders profit by buying currency pairs that have performed strongly recently and selling those that have performed poorly.

Implementation Steps:

  • Select a time window (e.g., 20, 50, or 200 days) to calculate momentum.
  • Compute the percentage price change over that window.
  • Set a momentum threshold and execute buy or sell orders when the change exceeds that threshold.
  • Recalculate momentum periodically and adjust positions accordingly.

Code example:

import pandas as pd
import numpy as np

data = pd.read_csv('forex_data.csv')

data['momentum'] = data['close'].pct_change(periods=20)

buy_threshold = 0.05
sell_threshold = -0.05

data['signal'] = np.where(data['momentum'] > buy_threshold, 1, 0)
data['signal'] = np.where(data['momentum'] < sell_threshold, -1, data['signal'])

data['return'] = data['close'].pct_change()
data['strategy_return'] = data['signal'].shift(1) * data['return']

2. Mean Reversion Strategy

This strategy is based on the assumption that market prices tend to revert to their mean over time. When prices deviate significantly from the mean, trades are executed in anticipation of a return to the average.

Implementation Steps:

  • Choose a time window to calculate the mean (e.g., 20 days).
  • Compute the mean and standard deviation.
  • Buy when the price drops below the mean minus a multiple of the standard deviation; sell when it rises above the mean plus that multiple.

Here’s the code:

data['mean'] = data['close'].rolling(window=20).mean()
data['std'] = data['close'].rolling(window=20).std()

buy_threshold = data['mean'] - 2 * data['std']
sell_threshold = data['mean'] + 2 * data['std']

data['signal'] = np.where(data['close'] < buy_threshold, 1, 0)
data['signal'] = np.where(data['close'] > sell_threshold, -1, data['signal'])

data['strategy_return'] = data['signal'].shift(1) * data['return']

3. Trend Following Strategy

Trend following strategies are based on the assumption that long-term trends exist in the market. When the price breaks a key level (like a moving average), the strategy takes a position in the direction of the trend.

Implementation Steps:

  • Select two moving averages with different periods (e.g., 50-day and 200-day).
  • Buy when the short-term MA crosses above the long-term MA; sell when it crosses below.

Here’s the code:

data['short_mavg'] = data['close'].rolling(window=50).mean()
data['long_mavg'] = data['close'].rolling(window=200).mean()

data['signal'] = np.where(data['short_mavg'] > data['long_mavg'], 1, 0)
data['signal'] = np.where(data['short_mavg'] < data['long_mavg'], -1, data['signal'])

data['strategy_return'] = data['signal'].shift(1) * data['return']