pythontrading

In trading, many strategies look great… until they face real market conditions.
That’s why backtesting is essential.
A proper backtest allows you to:
Measure not only ROI, but also risk (especially max drawdown).Understand how a strategy performs in different market regimes.Avoid overconfidence from “lucky trades”.
As a data scientist, I build Python trading bots with a simple philosophy:
👉 Returns are important, but risk management defines survival.
I use machine learning to optimize parameters, test setups, and evaluate the trade-off between higher returns and controlled drawdowns. My focus is not on predicting the future perfectly, but on creating strategies that remain robust when conditions change.
This account will share my journey in algorithmic trading: lessons learned, insights on risk management, and how data-driven approaches can give traders an edge.
Do you backtest your strategies, or do you rely more on intuition when trading?

— DrLegend —

#Backtesting #AlgorithmicTrading #machinelearning #Aİ #PythonTrading

Over the past few weeks, I've developed and refined an AI-powered Python signal generator bot to automate strategy detection and enhance trade decision-making — and I’m excited to share some impressive results and takeaways.
🤖 How It Works:
The bot scans live market data and uses a combination of candlestick patterns, price action zones, and multi-timeframe (MTF) trend analysis to generate actionable trade signals. It supports strategies like:
Bearish/Bullish EngulfingTweezer Tops/BottomsShooting StarMorning StarPeak Rejection (Zone & Candlestick based)Daily High/Low ZonesOnce identified, signals are ranked using key metrics like:Win RateProfit FactorPnL (Profit and Loss)Trade History & Breakdown
The bot then visualizes all this in a dashboard for live monitoring and rapid execution decisions.
📈 Strategy Leaderboard Highlights:
From the recent analysis:
Bearish Engulfing (Apex) stood out with 14 trades, 13 wins, 92.86% win rate, and a Profit Factor of 11.99, netting a total PnL of +268.78.
The Tweezer Top strategy remains perfectly accurate so far, with a 100% win rate across multiple trades.
Peak Rejection (Shooting Star) showed a decent 62.5% win rate and solid returns at +58.55 PnL.
These results help me filter and double down on what works — and cut underperforming strategies fast.
🔄 Live Bot in Action:
The bot is currently running real-time trades with auto-evaluation of MTF trend alignment and risk management via SL/TP. Here’s what it handles:
Leverage-specific entries per signalTrailing stop loss (TSL) handling and breakeven exits
Live position tracking with PnL and TP hit logs

Today’s top trade? A SHORT on MILKUSDT using Peak Rejection (Tweezer Top) with a whopping +45.36% PnL. ✅
🧠 Why Use This Bot?
✅ Cuts emotional bias from trading✅ Fast strategy iteration with real data feedback✅ Easy integration with Binance API✅ Helps focus only on high-probability setups
🛠️ Built With:
Python (Pandas, NumPy, TA-Lib, Plotly)Binance API (for live price & execution)Custom Signal Ranking & AI-generated Strategy Reports#PythonTrading #cryptosignals #BinanceTrading #BitcoinTreasuryWatch #BTCReserveStrategy @IRONMIND_BR @Square-Creator-861122001 @BLACKEAGLE_BESIKTAS @properlogic
$MEMEFI $PROVE $MYX