Algorithmic Trading: Build a Momentum Strategy in Python

Set up the Colab environment for the momentum strategy by installing packages, importing libraries, and configuring the FMP API keys, then test lookback and holding periods.

The update explains price data retrieval via a priority sequence: FMP if available, otherwise EOD or HD, then Yahoo Finance, with an option to force a source.

Define a comprehensive ETF universe, organize tickers by asset class, and download monthly pricing data from 2005 to 2025 for returns and momentum ranking analysis.

Analyze the ETF universe over time to count available funds, address survivorship bias, and plot ETF coverage for robust momentum backtesting.

Filter the ETF universe to 44 ETFs with data from 2008, then compute lookback period returns to rank past performance for a momentum strategy.

Explore momentum in ETFs by ranking 40 filtered ETFs using 1, 3, 6, 9, and 12 month returns, then analyze future performance of top versus bottom ranks to test momentum.

Compute forward returns from a 12-month lookback, rank etfs, and split them into ten deciles using a robust quantile cut, handling ties and nans to compare momentum performance.

Explore momentum investing with data visualization in Python, comparing top decile versus bottom decile using a 12-month lookback and 1-month forward returns, enhanced by pandas and seaborn charts.

Visualize top decile momentum across lookback and holding period pairs with a Python seaborn heat map. Rank past returns into deciles and compute average forward returns.

Examine momentum through visualizations of decile performance across lookback and holding periods, highlighting how decile one outperforms decile ten and forward returns, using Python for exploratory data analysis.

Code the momentum strategy in Python, using a 12-month lookback and a 1-month holding period, rank ETFs by momentum scores, and backtest against the S&P 500.

Code a 12-month lookback, 1-month holding momentum strategy across 58 ETFs in Python, using monthly returns and a benchmark for backtesting, then rank and select top ETFs with equal-weighted returns.

Develop and backtest a momentum strategy in Python by looping ETFs, shifting picks to avoid look-ahead bias, and tracking wealth growth against the S&P 500.

Debug the momentum backtest in Python by correcting three mistakes, align inputs for a 12-month lookback and 1-month holding period, and review results.

Finish coding the strategy and visualize monthly holdings with a seaborn heat map to show ETF selections over time, while noting backtest limitations.

Develop and backtest a momentum trading strategy in Python by collecting top ETFs, storing open-high-low-close data as pickle files, and performing a realistic daily backtest with robust data handling.

Implement a real backtest with slippage and a professional performance analysis for a long-term momentum ETF strategy, benchmarking against the S&P 500, and adapt for future strategies.

Build a custom back test that accounts for slippage, commissions, and daily trade logs to accurately evaluate a momentum strategy in Python.

Align month-end signals with actual trading days using pandas market calendars. Find the next trading day after signals, accounting for weekends, holidays, and NYSE.

This pro backtest IV lecture guides building a professional backtest with cash balance, holdings, daily market value, log files, and an open price, equal-weight rebalancing workflow.

Assemble net trade instructions from holdings and target tickers, compute each position's value, and simulate sells with open price, slippage, and risk-aware cash balance updates for backtesting.

Build a professional backtest in Python by modeling slippage, commissions, and real prices, manage cash for buys, track daily values, and produce a monthly final report with a benchmark.

Explore practical backtesting for a momentum strategy in Python by implementing open price extraction, monthly rebalances, and auditing trade logs to verify portfolio performance.

Analyze momentum strategy performance using tear sheets from monthly returns, assessing Sharpe and Sortino, benchmarking against the S&P 500, and exploring drawdowns and market capture.

Explore why momentum is a popular, robust anomaly across etfs, stocks, fixed income, commodities, currencies, and cryptocurrencies, and how yesterday's winners become tomorrow's winners.