Crypto momentum trading with backtesting

Derive the mathematical relationship between momentum and the autocorrelation of financial returns.

Differentiate between time-series and cross-sectional momentum, and identify the appropriate use cases for each in crypto markets.

Implement and compare various moving averages (SMA, EMA, WMA) for generating trend and momentum signals.

Construct a parameterized momentum signal function using lookback windows and return thresholds.

Apply smoothing techniques (e.g., using a secondary moving average) to a raw signal and analyze the trade-off between responsiveness and noise reduction.

Construct a vectorized backtesting engine in Python by aligning trading signals with forward returns to calculate strategy PnL.

Incorporate models for transaction costs and slippage based on a fixed percentage of trade value.

Prevent lookahead bias by ensuring all strategy calculations only use data available at the point of decision-making.

Model the impact of market frictions, such as exchange downtime and low-liquidity periods, on trade execution.

Calculate and visualize the cumulative equity curve and key drawdown statistics (max drawdown, duration).

Design test cases for the backtesting engine using synthetic data to verify the correctness of PnL calculations and bias prevention.

Formulate explicit mathematical conditions for strategy entry and exit rules based on generated momentum signals.

Integrate signal generation, entry/exit rules, and position sizing into a reusable object-oriented strategy class.

Implement dynamic position sizing models, including fixed fractional and volatility-targeting approaches.

Incorporate rule-based risk management techniques, such as fixed stop-losses and trailing stops.

Apply the Kelly criterion (full and fractional) to determine optimal capital allocation for a given momentum signal distribution.

Enforce portfolio-level risk constraints, such as maximum exposure per asset and total portfolio leverage.

Calculate and interpret risk-adjusted performance metrics (Sharpe, Sortino, Calmar ratios) considering the non-normal distribution of crypto returns.

Compare strategy performance against a buy-and-hold benchmark for the traded crypto assets.

Conduct walk-forward optimization to assess parameter stability and mitigate the risk of overfitting.

Generate a comprehensive backtest report including the equity curve, trade log, and summary statistics.

Perform a sensitivity analysis on key strategy parameters (e.g., lookback period, transaction costs) to understand their impact on performance.

Design a modular trading bot architecture separating data ingestion, signal generation, portfolio management, and order execution.

Implement a real-time data ingestion pipeline for crypto markets using WebSockets.

Develop a state management system to persist current positions, portfolio value, and open orders across system restarts.

Design a deployment strategy for a trading bot on Kubernetes, considering state persistence, secrets management, and fault tolerance.

Configure the bot to execute trades in a paper trading environment and implement robust error handling for order execution.

Develop a methodology to reconcile paper trading performance with backtest results, analyzing discrepancies from slippage, latency, and fees.