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+---- Thema: **Quantitative & Algorithmic Trading** (/showthread.php?tid=80)
**Quantitative & Algorithmic Trading** - indigostrader - 23.11.2025
### **Quantitative & Algorithmic Trading**
Welcome to the laboratory. This sub-forum is dedicated to those who speak in Python, R, and pinescript; who measure edge in basis points and significance; who trust only in rigorous backtesting and statistical validation. Here, we move beyond discretionary chart reading into the realm of systematic, rules-based trading strategies.
This is the domain of the quant—where market inefficiencies are identified, modeled, and exploited through code. If your trading involves data science, statistical arbitrage, or automated execution, you've found your home.
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* Building robust trading algorithms from first principles
* Proper backtesting methodology - avoiding overfitting and look-ahead bias
* Walk-forward analysis and out-of-sample testing
* Platform discussions: QuantConnect, Backtrader, MetaTrader, custom solutions
* Pairs trading and correlation-based strategies
* Cointegration tests and hedge ratio calculation
* High-frequency statistical arbitrage opportunities
* Market microstructure analysis
* Implementation shortfall and VWAP/TWAP strategies
* Latency optimization and exchange colocation
* Feature engineering for financial time series
* Random forests, gradient boosting, and neural networks for price prediction
* Reinforcement learning for optimal execution
* Data acquisition and cleaning (Tick data, OHLC, fundamental data)
* High-frequency data processing and storage
* Cloud deployment (AWS, GCP) vs on-premise solutions
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* "My mean reversion strategy shows 2.1 Sharpe in backtest but fails in live trading - help diagnose"
* "Comparing Kalman filter vs. OLS for dynamic hedge ratios in pairs trading"
* "Optimal portfolio construction for a basket of 20 algo strategies"
* "Has anyone successfully implemented a market-neutral factor model on crypto futures?"
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1. **Be Specific** - Include performance metrics, code snippets, or clear methodology
2. **Show Your Work** - Backtest results should include drawdowns, Sharpe, and sample size
3. **Discuss Edge** - What market inefficiency does your strategy exploit?
4. **Quantify Risk** - Always include risk management and position sizing methodology
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* _[STRATEGY SHOWCASE] Statistical Arbitrage on Crypto Futures - Full Backtest Results_
* _The Proper Way to Backtest: A Comprehensive Guide to Avoiding Biases_
* _Python vs. C++ for High-Frequency Trading: Performance Comparison_
* _My Open-Source Framework for Mean Reversion Strategies_
* _Machine Learning Feature Importance for Price Direction Prediction_
* _From Backtest to Live: My Journey Deploying an Algo on Interactive Brokers_
**Remember: In this forum, if you can't measure it, you can't trade it. Bring your data, your code, and your critical thinking.**
*Note: Basic "how-to-code" questions are better suited for general programming forums. The focus here should be on the application to trading systems.*