Stock Trading Mastery: Analysis & Algorithmic Coding

Access dedicated Jupyter notebooks for every lesson to test code, follow along, and experiment, while the next update stock finder notebook scans tickers by indicators to reveal trade opportunities.

Explore how the stock market operates, raising capital via shares and trading instruments like stocks, ETFs, and options across major exchanges, with volatility and regulatory risks.

Explore key market participants and their roles in the stock market, from retail and institutional investors to market makers, brokers, regulators, exchanges, investment banks, clearinghouses, analysts, and speculators.

Explore market mechanics by understanding how orders execute, bid and ask prices, and bid-ask spread, and how order types: market, limit, stop, stop limit, trailing stop affect costs and execution.

Master essential trading terminologies to analyze trends and manage risk, including bull and bear markets, liquidity, volatility, margin trading, support and resistance, fundamental and technical analysis, diversification, and market sentiment.

Set realistic, measurable trading goals aligned with your capital and time horizon to prevent overtrading and emotional decisions. Emphasize risk management, disciplined sizing, and progress reviews for sustainable long-term growth.

Interpret price action and market structure to anticipate opportunities and manage risk in financial markets, using trends, support and resistance, patterns, liquidity, order flow, and sentiment.

Learn how line, bar, and candlestick charts visualize price movements to identify trends and patterns, using open-high-low-close data and patterns like doji, hammer, and engulfing.

Explore indicators and oscillators such as moving averages, Bollinger Bands, RSI, MACD, and stochastic oscillator, including golden cross and death cross signals to forecast trends and reversals.

Explore how support and resistance, trendlines, and chart patterns illuminate price action, signal trade opportunities, and guide entry, exit, and risk management in technical analysis.

Master volume analysis to confirm price trends, identify reversals, and gauge market sentiment by examining volume patterns, divergences, and spikes, guided by volume moving average and OBV indicators.

Identify and avoid common pitfalls in technical analysis by integrating multiple indicators, market context, multiple time frames, volume, fundamental data, and disciplined risk management for informed trading decisions.

Apply fibonacci retracements and extensions to identify key support, resistance, and profit targets, confirm with moving averages, RSI, or MACD, and manage risk with strategic stop losses in trending markets.

Explore Bollinger Bands and volatility indicators to identify breakouts and reversals, using the middle SMA, upper and lower bands, and squeeze signals to manage risk.

Explore trend-following and mean-reversion strategies, using moving averages, MACD, ADX, Bollinger Bands, and RSI to adapt to trending, range-bound, and volatility and noise market conditions for disciplined risk-managed trading.

Combine technical and fundamental indicators to seek confluence, confirming trends and reversals while filtering noise, improving decision making and reducing risk in trading.

Develop a technical trading strategy by analyzing price movements, market patterns, indicators, and chart patterns; backtest and refine with moving averages, RSI, MACD, Bollinger bands, ATR, and risk controls.

Backtesting technical setups demonstrates testing strategies on historical data, using moving average crossovers, stop losses, and trailing stops in Python with Backtrader to refine risk and profitability.

Analyze income statements, balance sheets, and cash flow statements to evaluate revenue trends, profitability, liquidity, and debt; forecast earnings and build data-driven trading strategies.

Learn to use key financial ratios and valuation metrics—ROE, ROA, liquidity, debt, PE, PB, PEG—to assess financial health and guide buy, hold, or sell decisions.

Learn how macroeconomic factors—GDP, inflation, interest rates, employment, and consumer spending—shape markets and how traders combine these insights with technical analysis to time entries, exits, and risk.

Discover how to analyze industries and sectors using real-time data from Finviz, Yahoo Finance, and TradingView to identify momentum, sector rotation, and top stocks.

Earnings reports reveal revenue, expenses, net income, and earnings per share. Forward guidance, market reactions, and investor sentiment drive stock price movements; learn pre- and post-earnings strategies and risk management.

Explore news trading and market sentiment analysis to anticipate asset moves, applying pre- and post-news strategies, earnings and policy event insights, and solid risk management with technical entry points.

Master real-time industry and sector analysis to identify leading sectors, top industries, and high-probability stocks using Finviz, Yahoo Finance, and TradingView, with risk-managed long and short setups.

Master risk-reward ratio to evaluate trades by comparing potential profit to potential loss, using disciplined stop losses and position sizing to pursue long-term profitability.

Explore fixed fractional and fixed dollar sizing, volatility-based methods, and allocation strategies like equal weighting, risk parity, the Kelly criterion, and value at risk to manage risk and grow returns.

Master stop loss and take profit strategies to manage risk and lock profits, applying fixed, volatility-based, moving average, and trailing stops with Fibonacci and support and resistance targets.

Develop emotional discipline by recognizing fear, greed, and impulsivity in trading. Mitigate biases like confirmation, loss aversion, recency, and anchoring with a trading plan, risk controls, journaling, and mindfulness.

Develop a disciplined trading mindset by mastering emotional control, risk management, and a plan; mitigate cognitive biases and fear and greed, backtest strategies, and maintain a trading journal for consistency.

Explore how algorithmic trading leverages Python to automate strategies, enabling speed, efficiency, data-driven decisions, backtesting, and reduced emotional bias for improved trading performance.

Set up a Python environment for algorithmic trading by installing Python, VS Code, a virtual environment, and key libraries like NumPy, pandas, Matplotlib, yfinance, requests, and scikit learn.

Explore how Pandas, NumPy, and Matplotlib enable data manipulation, numerical operations, and visualization for stock prices and time series in algorithmic trading.

Learn to retrieve real time and historical financial data with APIs using Python, explore Yahoo Finance, Alpha Vantage, and restful endpoints, and visualize with Matplotlib for backtesting and analysis.

Master data pre-processing and visualization for algorithmic trading by cleaning and structuring financial data, then using Python tools like Pandas and Plotly to reveal trends with moving averages and charts.

Identify buy and sell signals in algorithmic stock trading through short-term and long-term moving average crossovers, such as 50/200 day, with backtesting, risk management, RSI, and volume filters.

Explore momentum-based trading strategies across stocks, forex, and crypto, using RSI, rate of change, and moving averages through algorithmic coding to identify entry and exit signals.

Explore mean reversion strategies in quantitative trading using moving averages, bollinger bands, and RSI to identify oversold or overbought conditions and backtest entry and exit rules.

Master how to use technical indicators in Python to analyze price movements and identify buy and sell signals for trading, including moving averages, RSI, Bollinger bands, and on balance volume.

Backtesting algorithmic strategies use historical data to simulate trades, assess profitability and risk with metrics like CAGR and Sharpe ratio, while highlighting moving average crossover and mean reversion techniques.

Discover how machine learning reshapes trading by analyzing data, predicting stock prices, and enabling fast, automated decisions through supervised, unsupervised, and reinforcement learning.

Explore feature engineering for stock data, transforming price and volume into indicators like returns, moving averages, volatility, volume weighted average price, and relative strength index to guide algorithmic trading.

Build predictive models for price forecasting with machine learning, covering supervised and unsupervised methods. Explore feature selection, preprocessing, training and validation, plus a Python linear regression example forecasting 20 days.

Evaluate machine learning models for trading using the right metrics and backtesting to ensure robust real-world performance. Apply cross-validation, regularization, feature selection, and aware of overfitting and data snooping biases.