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Backtesting Frameworks

Validate trading strategies on historical data before risking capital

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Backtesting frameworks simulate trading strategies on past data. Mid-level quants earn $140-180k; senior quant strategists command $250-350k at hedge funds.

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Backtesting frameworks are software tools that simulate trading strategies on historical price data, calculating performance metrics (returns, drawdown, Sharpe ratio) without real capital. Popular frameworks include Backtrader, VectorBT, Zipline, and QuantConnect. Backtesting identifies strategy edge before live trading, reveals transaction cost impact, and enables walk-forward analysis to detect overfitting. - Risk reduction: Test extensively before risking real money

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Chartered Financial Analyst (CFA)
Financial Data Science certification

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Quantitative Analyst

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â–ļWhat's backtesting?
Running trading strategy on historical data to evaluate performance before live trading.
â–ļWhat's overfitting in backtesting?
Optimizing parameters on historical data so well that strategy fails on new data. Use out-of-sample testing.
â–ļWhat does Backtrader provide?
Python framework for designing and testing strategies. Handles data feeds, order execution, portfolio tracking.
â–ļHow do I avoid lookahead bias?
Ensure indicators use only past data (no future prices). Check backtest implementation carefully.
â–ļWhat are transaction costs?
Commissions, slippage, bid-ask spread. Critical to include; can wipe out strategy profitability.
â–ļHow many trades do I need for statistical significance?
At least 30-50 trades minimum for reasonable accuracy. More is better; 100+ trades preferred.

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