Technical analysis: Backtesting the stock market

Backtesting tests a trading method on past data to estimate future performance and risk. The lesson covers trading concepts, time frames, and setup with free and premium databases on macOS.

Backtesting tests a trading system on past data to assess profitability and risk, allowing adjustments or scrapping based on performance between point a and point b.

Backtesting demonstrates whether indicators and strategies hold up against past data, quantifying risk and maximum drawdown ahead of time. It also strengthens trading psychology and confidence to trade real money.

Backtesting offers probabilistic insights into potential profits and losses, but past performance does not guarantee future results; use it to inform decisions in dynamic markets.

Explore what constitutes an algorithmic trading system: a rule-based, entry/exit signal framework using technical analysis of past price movement to automate profits, especially with backtesting on end-of-day data.

Explore the time frame options for backtesting, including high-frequency, intraday, and end-of-timeframe approaches. Assess how your available time to monitor trading or investments shapes your time frame decision.

Explore the high frequency timeframe and how razor thin profits accrue through rapid trades near stock exchange servers, while risks like spoofing and manipulation challenge traders.

Explore intraday time frames for day traders and aim to exit all positions by end of the day; if you can't sit at a PC all day, avoid intraday trading.

Learn to backtest stock market strategies using the end-of-day time frame, compare holding periods from one to six months, and apply patience to realize longer-term profits.

Define your testing universe across Nasdaq, NYSE, and SGX, then select a historical time period and time frame for backtesting to assess whether past patterns remain exploitable.

Discusses asymmetry of returns, illustrating how losses and gains are not equal, and advocates cutting losses quickly to preserve capital while targeting profits.

Explore how diversification across many stocks reduces portfolio risk in a coin game analogy, showing that concentrating bets can lead to large losses despite favorable odds.

Assess backtesting by weighing time commitment, risk appetite, capital limits, and personal goals to align your trading strategy with the ability to sleep at night.

True portfolio testing contrasts single-stock backtests with portfolio-level evaluation, showing how cash allocation, diversification, and purchase decisions shape overall trading performance.

Learn how to test trading systems consistently and interpret the equity curve shape to assess performance in backtesting the stock market.

Explore how annualized returns and the compound growth rate (CAGR) measure performance over a time period, highlighting the importance of compounded results and time-weighted returns for apples-to-apples comparisons.

Analyze how the trading system win rate relates to profitability, showing that high win rates can accompany portfolio losses, while psychology and position management influence trend-based outcomes.

Explore how consecutive wins and losses affect a trading system, highlighting psychological stress and the challenges revealed through backtesting.

Discover how equity curve shape reveals trading system performance, highlighting ideal uptrends with occasional bumps, common zigzag patterns, and the caution against zero-return strategies that fail to capture market efficiency.

This lecture introduces basic programming concepts and building blocks, explains how these transferable skills apply across languages like C++, Java, Python, and prepares beginners for backtesting in stock market analysis.

Define variables as storage spaces for numbers, assign values like 25 and 35, and compute their sum to 60, then update and reuse them across code.

Master conditional logic in programming for backtesting the stock market by testing numeric comparisons, evaluating true or false outcomes (1 or 0) with variables.

Apply all-condition logic to backtesting by combining multiple criteria with and/or operators, evaluating expressions to true or false, and learning how to ensure all conditions are met for trades.

Explore Pogrebin syntax, including line-based expressions terminated by semicolons and how data separation marks the end of an expression, noting that different Pogrebin languages use distinct syntax.

Identify and avoid using keywords as variables to prevent clashes with the system. The lecture highlights reserved words like or, by, and cell, and shows how editors warn against misuse.

Learn to use function calls to set options and define equity in backtesting, showing how a task becomes a parameterized, reusable operation.

Explain how to document backtesting code with comments that separate machine logic from human notes, clarifying each line’s purpose and how equity is set in the backtest.

Explore the tools for backtesting the stock market, from server and broker software to a comprehensive historical price database, and how to download and port data.

Learn how to download and install Amibroker, a backtesting software, and use its portfolio testing, Monte Carlo simulations, and customizable charts and indicators to analyze stocks.

Learn how to use free US daily stock data for backtesting, including selecting Nasdaq and NYSE stocks, downloading and importing data, and preparing a backtest setup.

Learn how to use ShareInvestor data to download and extract historical stock data—Nasdaq, NYSE, and SGX—with daily high, low, and volume, and load it into broker software.

Explore the hands-on process of backtesting the stock market, including setting backtest parameters, position sizing, equity allocation, and accounting for commissions.

Set up a backtesting engine to test signals with market-specific position sizes (one share vs 100 shares) and account for commissions, liquidity, and testing conditions.

Learn how to set up a backtesting framework by defining a trading system with buy and sell signals, using open prices for execution, and timing entries and exits across days.

Explore how simple and exponential moving averages identify trends and support or resistance, and acknowledge that crossovers lag yet generate buy or sell signals.

Examine a backtested moving average crossover strategy using 50- and 200-day averages, with signals from closing price crossings. Assess performance, drawdowns, and CAGR trends in US data.

Explore how the relative strength index signals price strength and turns within a 0–100 range, with oversold around 30 and overbought around 70 guiding buys and sells.

Explore hands-on relative strength index (RSI) backtesting in a mechanical RSI trading system, using 70 and 30 thresholds to signal buys and sells, and review performance, drawdowns, and equity curves.

Backtest a random coin toss entry with a 50% trading stop, set a 30% profit target, and exit on the open the fourth day to take profits.

Explore backtesting methods for stock trading using random coin toss entries and various stop types (percent stop loss, profit target, trading stop, end stop), and interpret performance outcomes.

Learn how backtesting assesses stock market trading systems by measuring performance, drawdown, and equity, and compare strategies like moving averages, RSI, and random entries using a database and testing software.