The number one mistake algo traders make is deploying a bot without backtesting it first. Backtesting will not guarantee future performance, but it will tell you whether your strategy has ever worked.
Clean data, realistic fills with slippage and fees, walk-forward validation, and correct compounding position sizing.
import requests
import pandas as pd
def fetch_ohlcv(symbol, resolution, days=90):
url = "https://api.lmex.io/futures/api/v2.3/ohlcv"
r = requests.get(url, params={
"symbol": symbol,
"resolution": resolution,
"limit": 200
})
data = r.json()
df = pd.DataFrame(data, columns=["time","open","high","low","close","volume"])
return df.astype({"open":float,"high":float,"low":float,"close":float})
Split your data into thirds. Train on the first two thirds, test on the final third. If the strategy works on the test set with similar metrics, you have a robust edge. Never optimise parameters on your test set.
Q: How much historical data do I need to backtest a LMEX trading strategy?
A minimum of 90 days is needed for hourly strategies. For daily strategies, use 1-2 years. Always reserve the final 20-30% of your data as an out-of-sample test set that you never touch during optimisation.
Q: What is walk-forward testing and why is it important for crypto bots?
Walk-forward testing splits your data into rolling windows, optimising on each window and testing on the next. This simulates real trading conditions and catches overfitting that standard backtests miss. It is the gold standard for validating crypto trading strategies before deployment.
Q: How do I account for slippage and fees in my LMEX backtests?
LMEX charges 0.01% for futures taker orders. Add a realistic slippage estimate of 0.05-0.1% per trade depending on order size relative to order book depth. Always include these costs or your backtest results will be significantly overstated.
Q: Should I use tick data or OHLCV data for backtesting LMEX strategies?
OHLCV data (1m candles minimum) is sufficient for most swing and trend-following strategies. For high-frequency or market-making strategies, tick data gives more accurate results but requires significantly more processing.