Scafolding for basic trading bot

wolf.py

import requests
import numpy as np

class MockExchangeAPI:
    def get_latest_price(self, symbol):
        # Mock function to get the latest price. Replace with actual API call.
        return requests.get(f"https://api.exchange.com/latestPrice?symbol={symbol}").json()

    def execute_trade(self, symbol, trade_type, quantity):
        # Mock function to execute a trade. Replace with actual API call.
        print(f"Executing {trade_type} trade for {quantity} of {symbol}")

class TradingBot:
    def __init__(self, symbol, short_window, long_window, api):
        self.symbol = symbol
        self.short_window = short_window
        self.long_window = long_window
        self.api = api
        self.prices = []

    def calculate_ema(self, prices, window):
        return np.mean(prices[-window:])  # Simplified EMA for demonstration

    def fetch_latest_price(self):
        latest_price = self.api.get_latest_price(self.symbol)
        self.prices.append(latest_price)
        return latest_price

    def evaluate_signals(self):
        if len(self.prices) >= self.long_window:
            short_ema = self.calculate_ema(self.prices, self.short_window)
            long_ema = self.calculate_ema(self.prices, self.long_window)

            if short_ema > long_ema:
                return "buy"
            elif short_ema < long_ema:
                return "sell"
        return "hold"

    def execute_trade(self, signal):
        if signal == "buy":
            self.api.execute_trade(self.symbol, "buy", 1)  # Example quantity
        elif signal == "sell":
            self.api.execute_trade(self.symbol, "sell", 1)

    def run(self):
        while True:
            latest_price = self.fetch_latest_price()
            signal = self.evaluate_signals()
            self.execute_trade(signal)

if __name__ == "__main__":
    api = MockExchangeAPI()
    bot = TradingBot("BTC-USD", short_window=10, long_window=50, api=api)
    bot.run()