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21 Artikel in #quant-trading werden angezeigt

#quant-trading

Quant Research Workflow: Jupyter Notebooks and Feature Engineering

A repeatable quant research workflow using Jupyter notebooks covers feature engineering, validation, and notebook hygiene that produces deployable signals.

#algorithmic#quant-trading

Build a Python Trading Script With ccxt and vectorbt

A working Python trading script template combining ccxt for exchange data and vectorbt for fast vectorized backtests, with concrete code you can adapt.

#algorithmic#quant-trading

Machine Learning Trading Pitfalls and Concrete Failures

Concrete machine learning trading pitfalls — data leakage, regime decay, overfitting — with specific failure patterns and numeric thresholds to detect them.

#algorithmic#quant-trading

High Frequency vs Low Frequency Strategy Choice

Choosing between high frequency and low frequency trading strategies depends on costs, latency, edge decay, and capacity, with concrete tradeoffs for each.

#algorithmic#quant-trading

Broker API Integration Practice: Exchange, IBKR, FXCM

Practical broker API integration for exchanges, Interactive Brokers, and FXCM covers rate limits, websocket reconnection, and error handling patterns.

#algorithmic#quant-trading

Algo Strategy Monitoring and Exception Handling in Production

Production algo monitoring and exception handling covers alert thresholds, error classification, kill switches, and recovery procedures for live systems.

#algorithmic#quant-trading

Algo Strategy Deployment Architecture: Signal to Risk to Execution

A production algo deployment architecture separating signal generation, risk gates, and execution, with concrete components and data flow for live trading.

#algorithmic#quant-trading

Statistical Arbitrage and Pairs Trading

Statistical arbitrage exploits temporary mispricings between related assets. Learn the math of cointegration, how pairs trading works, and where it breaks.

#algorithmic#quant-trading

Signal Generation and Factor Models

Signals are the heart of any quant strategy. Learn what makes a good signal, how factor models organize them, and why most "signals" aren't.

#algorithmic#quant-trading

Quant Trading Framework: Alpha, Beta, and Signals

Every quant strategy reduces to the same framework: a signal that generates alpha, sized against beta. Learn the vocabulary that organizes all of quant trading.

#algorithmic#quant-trading

Python for Trading: pandas, numpy, backtrader

Python is the workhorse of quant research. Learn the three libraries every trader needs — pandas, numpy, and backtrader — and what each one does.

#algorithmic#quant-trading

Trading Languages: Pine, Python, MQL, C# Compared

Different trading platforms speak different languages. Learn what Pine, Python, MQL, and C# are good at — and which one matches your goals.

#algorithmic#quant-trading