Complete Course on Trading Statistics | Trading Masterclass

Explore mean versus median in trading statistics, compute averages from returns, arrange values to find the middle value, and contrast distribution effects with portfolio implications.

Discover how percentiles assess where a stock's return stands in a group, identify the 90th percentile in a nine-strategy portfolio, and flag outliers for trading decisions.

Define standard deviation as dispersion around the mean. Compare two trading strategies by return and variability, highlighting low risk with smoother equity curves versus higher return with more volatility.

Understand how two stocks move in relation through correlation, showing same direction, opposite direction, or zero correlation, with values ranging from plus 1 to minus 1.

Learn to compute mean, median, percentiles, and standard deviation from Google stock data in Google Spreadsheets, then calculate daily returns and correlations with charts.

Explain how the central limit theorem shows that the means of many stock returns tend toward a normal distribution, enabling traders to predict data from a bell curve.

Explore the normal distribution in trading, linking mean and standard deviation to the bell curve. Discover that 68% and 95% of returns lie within one and two standard deviations.

Use a mean-based strategy with a 45-day mean and standard deviation to bound Google stock, for educational purposes, buying on opens below the lower bound and selling at close.

Analyze trading strategy performance using metrics such as total profits, capital employed (100k), and 23.6% return, plus total trades, total wins, win rate, and averages on winning and losing days.

Assess strategy performance with metrics and the sell rule, which is when the open price exceeds the mean plus standard deviation, and compare backtest returns to Google stock.

Explore the expectancy ratio to gauge a trading strategy's profitability by weighing win ratio, average profit, and average loss, and learn how to interpret the report card for strategy decisions.

Learn to compute and plot an equity curve showing how starting capital evolves with daily returns, including drawdowns, and compare it with external data like Google's closing prices.

Explore how maximum drawdown quantifies the drop in portfolio value along the equity curve, illustrated with a $100 to $180 to $130 to $200 example to compare risk across strategies.

Explore the Sharpe ratio, a performance measure comparing returns to risk. Compute daily returns, use the risk-free rate and standard deviation, and interpret thresholds for good or very good performance.

Learn how simple moving averages smooth price data, identify trends, and signal crossovers, with examples using 20-day and 50-day SMAs and a spreadsheet calculation.

Explore the exponential moving average in trading statistics, where a smoothing factor weights recent prices for a more responsive indicator than the sma, with spreadsheet calculations.

Discover bollinger bands to gauge volatility, define trading range, and spot overbought or oversold signals using a 20-day sma and bands at two standard deviations.

The relative strength index measures speed of recent price moves to flag overbought or oversold conditions, using average gains and losses over a chosen period with 80 and 20 thresholds.

Discover how average true range measures stock volatility and guides stop losses and position sizing. Learn the ATR calculation using prior ATR, true range, and a practical Apple example.

Learn how vwap weights price by volume to reflect large trades, calculating price times volume divided by total volume from session start, applicable to intraday or daily charts.