algorithmictrading

From Zero Signals to Production-Ready: How We Built a Smart Money Concepts Trading Bot
The Beginning: A Bold Vision 💡
It all started with a simple question: "Can we build a trading bot that thinks like institutional traders?"
We wanted to create something different—not another indicator-based bot, but a system that understands Smart Money Concepts (SMC) and follows the same logic that big players use in the market.
The Challenge: Zero Signals Over a Year 📉
After months of development, we hit a wall. Our bot was working perfectly—except it found zero signals for BTC, ETH, and DOGE over an entire year of backtesting.
The problem? We built it like a textbook SMC system, expecting perfect institutional execution with ideal retracement points. But crypto markets don't work that way—especially large-cap coins that move fast and rarely give you "perfect" setups.
The Breakthrough: Dual Entry Models 🎯
Instead of giving up, we redesigned the system with two entry models:
Entry Model A: Classic SMC (The Perfectionist)
Clean Fair Value Gaps (FVG)Deep retracements to Order Blocks (OB)Rare, high-quality signalsPerfect for institutional-style tradingEntry Model B: Crypto-Compatible (The Realist)Shallow FVG taps (30-50% fill)Displacement pullbacks (0.2-0.35× ATR)First opposing candle OB (even 1-candle)Designed for fast-moving crypto markets
The magic: Each scanner can choose its own model—Classic Only, Crypto Only, or Both (with fallback logic).The Architecture: 5-State Validation System ⚙️Every signal must pass through 5 strict gates:HTF Context - Higher timeframe trend and value zonesLiquidity Event - Price sweeps liquidity with volume confirmationCHoCH - Change of Character (intent reversal confirmed)Entry Model - Classic or Crypto-compatible POI detectionExecution - Risk-reward validation (minimum 2:1 R:R)
No shortcuts. No compromises. If any gate fails, the signal is rejected.Three Scanners, Three Personalities 🎭1. BALANCED_SCM (The All-Rounder)15-minute timeframeScans every 2 minutesMax 12 concurrent signalsUses both entry models (Classic first, Crypto fallback)Perfect for most traders2. SNIPER_SCM (The Precision Hunter)5-minute timeframeScans every 1 minuteMax 6 concurrent signalsClassic SMC only (strictest quality)Minimum 2.5:1 R:RFor experienced traders who want perfection3. HIGHRISKRETURN_SCM (The Big Game Hunter)15-minute timeframeScans every 10 minutesMax 4 concurrent signalsCrypto-compatible model (optimized for BTC/ETH/DOGE)Minimum 3:1 R:RSignals valid for 72 hoursFor traders who want high-quality, high-R:R setupsThe Technical Stack 🔧Python - Core logic and state machineBinance Futures API - Real-time market dataMulti-timeframe Analysis - 1m to 1d integrationSession-Based Filtering - London & New York sessionsBacktesting Engine - Historical validation with professional reportsYAML Configuration - Easy scanner customizationStructured Logging - Production-ready monitoringKey Features That Make It Special ✨🎯 Value Proximity GuardrailsEven with relaxed crypto rules, we prevent chasing:Hard block at HTF extremes (no buying into highs, no selling into lows)Proximity checks at POI tap (not at CHoCH)Single tolerance source (0.15-0.25× HTF_ATR)📊 Professional BacktestingInteractive backtest selectionCustom date rangesComprehensive reports with:Win rate by scannerEntry model performance breakdownProfit factor analysisDetailed trade logs🔄 Production-ReadyProcess management (no conflicts)Structured loggingError handlingClean codebase (no dead code)Full documentationThe Results: What We Learned 📈Scarcity is a feature, not a bug - Fewer signals = higher qualityCrypto needs crypto logic - Textbook SMC doesn't always workGuardrails prevent edge erosion - Safety checks are non-negotiableIteration beats perfection - We rebuilt Entry Model 4 times before getting it rightWhat's Next? 🚀Live Testing - Deploying to productionPerformance Monitoring - Tracking real-world resultsCommunity Feedback - Learning from actual tradersContinuous Improvement - Refining based on market behaviorFinal Thoughts 💭Building this bot taught us that trading systems aren't just code—they're market models. Every decision matters. Every gate counts. And sometimes, the best solution is to build two solutions and let the market choose.
To all the traders out there: Whether you're using indicators, SMC, or pure price action—remember that the best system is the one you understand and can execute consistently.Want to Learn More? 📚We've documented everything:5-state validation logicEntry model configurationsScanner setup guidesBacktesting workflowsProduction deployment checklists

This isn't just a bot—it's a complete trading system.

Built with Python, powered by Binance Futures API, validated by backtesting, and refined through collaboration.

#TradingBots #smc #cryptotrading #BinanceFutures #AlgorithmicTrading

Disclaimer: This is a signal generation system, not automated trading. Always do your own research and use proper risk management. Trading involves risk of loss.

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

$BTC
#DepthOfMarket
#AlgorithmicTrading
#SmartTrading
#DOMAnalysis
#NextGenTrading

Introduction: Why be interested in the order book (DOM)?
Most traders scrutinize price charts, moving averages, and even a few classic technical indicators… But how many actually take the time to analyze the order book (Depth of Market, or DOM)?

Yet this is where it all begins: every price movement results from an imbalance between supply and demand in this order book. If an aggressive buyer decides to take all the sell orders at a given level, the price goes up. If, on the contrary, massive selling pressure absorbs the buyers, the price falls.

Dear Friends 💞💞💞💞
When I first started trading large volumes, I quickly ran into a big challenge: juggling multiple sub accounts, placing massive orders, and constantly worrying about my balance taking a hit. It was like playing a high-stakes game one wrong move, and all my ₿ $BTC gains could disappear in an instant.
That’s when I decided to try out tools designed for market makers and algorithmic traders. I went with the WhiteBIT Market-Making Program after hearing about its benefits……….

My Journey:
I set up one strategy for spot trading, another for futures, and an arbitrage plan across different trading pairs. The API executed my orders in a flash , WebSocket gave me real-time access to the order book, and webhooks alerted me to any changes in my balance. What used to take hours of manual work is now fully automated and clear……...
The result? A smoother, faster, and more efficient trading experience………..
#CryptoTrading #MarketMaking #AutomatedTrading #FuturesTrading #AlgorithmicTrading