Technical Analysis with Python

Understand auction market theory, price as a sufficient statistic, and why most technical strategies fail

Learn bid-ask spreads, slippage, order types, and how real-world execution costs impact strategy profitability

Master stationarity, autocorrelation, volatility clustering, and why normal distribution assumptions fail for financial data

Master the OHLCV format, timeframe aggregation, and how to extract information from candlestick data

Learn to download, validate, and manage financial data using Python and yfinance

Decode candlestick structure as compressed order flow and build candlestick features programmatically

Implement classic patterns algorithmically and test their actual predictive power with data

Detect uptrends, downtrends, and sideways markets algorithmically using swing points and trend strength

Quantify trend momentum and quality using slope, rate of change, regression analysis, and composite scores

Identify and validate support/resistance levels using swing points, clustering, and bounce rate testing

Detect high-probability trading zones using volume profile, POC, value areas, and order flow imbalances

Detect trend continuation and reversal signals using Break of Structure and Change of Character patterns

Understand MAs as low-pass filters, lag-smoothness tradeoffs, and optimal period selection

Implement Simple and Exponential Moving Averages from first principles and compare properties

Build and backtest MA crossover systems, golden/death cross, and MA ribbon strategies

Implement RSI from scratch, understand overbought/oversold levels, and test divergences

Build MACD from scratch, interpret histogram, and test signal line crossovers

Implement stochastic oscillator, test overbought/oversold signals, and compare to RSI

Measure volatility using historical methods, understand volatility clustering and regimes

Implement Bollinger Bands and ATR, use for position sizing and stop-loss placement

Understand volume as market participation, detect accumulation/distribution, and volume climaxes

Implement On-Balance Volume, VWAP, and volume-weighted indicators for confirmation

Combine volume and price action for high-probability entries and avoid false breakouts

Convert trading ideas into explicit entry/exit logic with defined parameters and risk rules

Build rule-based trading systems with entry, exit, position sizing, and stop-loss logic

Design event-driven backtesting systems that model realistic execution and avoid common pitfalls

Implement a complete backtesting engine with transaction costs, slippage, and position tracking

Analyze equity curves, drawdowns, trade distribution, and identify overfitting red flags

Calculate and interpret risk-adjusted return metrics for strategy comparison

Explore advanced metrics: omega ratio, information ratio, and tail risk measures

Implement position sizing algorithms that optimize risk-reward and prevent ruin

Design stop-loss strategies, profit targets, and trailing stops with statistical validation

Detect overfitted strategies using parameter heat maps and robustness testing

Implement walk-forward optimization to test strategy robustness on unseen data

Build regime classification systems using volatility, ADX, and hidden Markov models

Create strategies that adapt parameters to changing market conditions automatically

Analyze market structure across multiple timeframes for trend and tactical alignment

Build strategies that use higher timeframes for direction and lower for precise entry

Apply t-tests, permutation tests, and bootstrapping to validate trading edges

Use Monte Carlo methods to estimate strategy risk, maximum drawdown, and probability of ruin

Design a complete trading system: hypothesis, indicators, entry/exit rules, and risk management

Implement, backtest, validate, and defend your complete professional-grade trading system