Trend Following Models

Introduction

Trend following is a strategy that aims to capitalize on the continuation of existing market trends. Whether the market is bullish or bearish, trend-following models seek to enter trades at opportune times to follow the direction of the market. This method has stood the test of time, consistently producing substantial returns for those who have mastered its application.

Core Concept

At its heart, trend following relies on the belief that prices will continue to move in the same direction they have been moving rather than reversing course. This approach contrasts with mean reversion strategies, which bet that prices will return to a long-term average.

Key Components

Market Data

To effectively build and execute a trend-following strategy, accurate and timely market data is crucial. Traders need access to:

Indicators

Indicators are essential tools in identifying trends. Some popular indicators include:

Algorithms

Algorithms are at the core of trend following in algorithmic trading. These are rule-based systems designed to automatically implement the trading strategy.

Trend Following Models

Moving Average Crossover

Description: This model uses two moving averages of different time periods. A buy signal is generated when the shorter-term moving average crosses above the longer-term moving average. Conversely, a sell signal is generated when the shorter-term moving average crosses below the longer-term moving average.

Code Example:

def moving_average_crossover(prices, short_window, long_window):
    short_ma = prices.rolling(window=short_window, min_periods=1).mean()
    long_ma = prices.rolling(window=long_window, min_periods=1).mean()

    signals = pd.DataFrame([index](../i/index_instrument.html)=prices.[index](../i/index_instrument.html))
    signals['signal'] = 0.0
    signals['short_ma'] = short_ma
    signals['long_ma'] = long_ma
    signals['signal'][short_window:] = np.where(signals['short_ma'][short_window:] > signals['long_ma'][short_window:], 1.0, 0.0)
    signals['positions'] = signals['signal'].diff()

    [return](../r/return.html) signals

Bollinger Bands

Description: Bollinger Bands consist of a middle band (simple moving average) and two outer bands that are calculated using standard deviations. When the price hits the lower band, a buy signal is generated. When the price hits the upper band, a sell signal is generated.

Code Example:

def bollinger_bands(prices, window, num_std_dev):
    rolling_mean = prices.rolling(window=window).mean()
    rolling_std = prices.rolling(window=window).std()
    
    upper_band = rolling_mean + (rolling_std * num_std_dev)
    lower_band = rolling_mean - (rolling_std * num_std_dev)
    
    signals = pd.DataFrame([index](../i/index_instrument.html)=prices.[index](../i/index_instrument.html))
    signals['price'] = prices
    signals['upper_band'] = upper_band
    signals['lower_band'] = lower_band
    signals['rolling_mean'] = rolling_mean
    
    signals['buy_signal'] = np.where(prices < lower_band, 1.0, 0.0)
    signals['sell_signal'] = np.where(prices > upper_band, -1.0, 0.0)
    
    [return](../r/return.html) signals

Relative Strength Index (RSI)

Description: The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is typically used to identify overbought or oversold conditions. An RSI above 70 indicates overbought, while an RSI below 30 indicates oversold.

Code Example:

def relative_strength_index(prices, window):
    [delta](../d/delta.html) = prices.diff()
    [gain](../g/gain.html) = ([delta](../d/delta.html).where([delta](../d/delta.html) > 0, 0)).fillna(0)
    loss = (-[delta](../d/delta.html).where([delta](../d/delta.html) < 0, 0)).fillna(0)

    avg_gain = [gain](../g/gain.html).rolling(window=window, min_periods=1).mean()
    avg_loss = loss.rolling(window=window, min_periods=1).mean()

    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))
    
    signals = pd.DataFrame([index](../i/index_instrument.html)=prices.[index](../i/index_instrument.html))
    signals['price'] = prices
    signals['rsi'] = rsi

    signals['buy_signal'] = np.where(rsi < 30, 1.0, 0.0)
    signals['sell_signal'] = np.where(rsi > 70, -1.0, 0.0)

    [return](../r/return.html) signals

Risk Management

Effective trend-following involves robust risk management techniques, including:

Conclusion

Trend-following models are foundational strategies in the world of algorithmic trading. By understanding and implementing different types of trend-following models, such as Moving Average Crossover, Bollinger Bands, and RSI, traders can make informed decisions and potentially capitalize on existing market trends.