Trend following

Trend following is a trading strategy that aims to capitalize on the momentum of price movements. In the context of a market-making bot, trend following involves identifying and trading in the direction of prevailing market trends. This can help the bot make more informed decisions about when to buy or sell assets.

▎How Trend Following Works

1. Identifying Trends:

   • The bot uses technical indicators (like moving averages) to determine the direction of the market.

   • A trend is typically classified as either upward (bullish), downward (bearish), or sideways (neutral).

2. Trading Decisions:

   • If the market is in an uptrend, the bot may place buy orders to capitalize on rising prices.

   • Conversely, in a downtrend, it may place sell orders or short positions.

3. Risk Management:

   • Trend-following strategies often include stop-loss orders to limit potential losses if the trend reverses.

   • Position sizing and diversification can also help manage risk.

▎Example Code for Trend Following Feature

Here’s a simple implementation of a trend-following feature within a market-making bot using Python:

import time import random

class TrendFollowingBot: def init(self, short_window=5, long_window=20): self.short_window = short_window # Short-term moving average window self.long_window = long_window # Long-term moving average window self.prices = [] # Store historical prices

def get_current_price(self):
    # Simulated price retrieval
    return random.uniform(95, 105)  # Simulating price around $100

def calculate_moving_average(self, window):
    if len(self.prices) < window:
        return None
    return sum(self.prices[-window:]) / window

def check_trend(self):
    current_price = self.get_current_price()
    self.prices.append(current_price)

    short_ma = self.calculate_moving_average(self.short_window)
    long_ma = self.calculate_moving_average(self.long_window)

    print(f"Current Price: {current_price:.2f}, Short MA: {short_ma:.2f}, Long MA: {long_ma:.2f}")

    if short_ma and long_ma:
        if short_ma > long_ma:
            self.execute_trade("BUY", current_price)
        elif short_ma < long_ma:
            self.execute_trade("SELL", current_price)

def execute_trade(self, action, price):
    print(f"Executing {action} at {price:.2f}")

def run(self):
    while True:
        self.check_trend()
        time.sleep(1)  # Wait for a second before checking again

Example usage

if name == "main": bot = TrendFollowingBot(short_window=5, long_window=20) bot.run()

▎Explanation of the Code

• TrendFollowingBot Class: This class implements a basic trend-following strategy.

• get_current_price(): Simulates retrieving the current market price. In a real-world application, this would interface with an API for live data.

• calculate_moving_average(window): Calculates the moving average for the specified window. It returns None if there aren’t enough data points.

• check_trend(): Retrieves the current price, updates the historical prices list, calculates the short-term and long-term moving averages, and checks for trading signals based on their relationship.

• execute_trade(action, price): Executes the trade by printing the action (buy/sell) and the price.

• run(): Continuously checks for trends every second.