Performance Optimization and Risk Management for Trading

Learn to calculate sustainable trading income by applying the formula initial capital times trading return times (1 minus tax) minus inflation, and apply a 20-50% adjustment for risk and timing.

Assess how to make a living with trading: capital, risk tolerance (ability and willingness to take risk), and living costs, plus leverage, tax regimes, and regulatory limits.

This illustrative example estimates the initial trading capital required to generate a net one thousand dollar monthly income, with 100% annual return, 25% tax, and 3% inflation protection.

Install Python and numerous packages with an updated Anaconda setup, avoid the default Mac Python, and explore editors like Spyder or Visual Studio Code for scientific data work.

Install the anaconda distribution to get python and a data science environment with pre-installed packages for dependency management. Enjoy jupyter notebooks and ides with cloud or local install options.

Install and import the yfinance library to load data from Yahoo! Finance into Python, using the Anaconda prompt and pip install yfinance after updating conda for web data retrieval.

Load historical stock data with pandas and yfinance by setting a date range and ticker (BA), producing a data frame with open, high, low, close, adjusted close, and volume.

Load and transform a multi-asset data frame in pandas, set a datetime index, and extract close prices for visualization. Compare instrument performance with price charts and basic statistics.

Compare instruments using relative price changes, or financial returns, and compute percentage changes to show that returns are meaningful and comparable across price levels.

Learn how compound returns and the geometric mean return are computed from daily returns, and compare them with arithmetic mean return to highlight reinvested profits and the proper multiplier.

Analyze discrete compounding by comparing annual, quarterly, and monthly interest, calculating future value and effective annual rate, and understanding compound interest and the transition toward continuous compounding.

Compare simple returns and log returns using a two-year price path, showing how log returns are additive over time and prevent misleading arithmetic averages.

Compare simple and log returns for investment multiple, normalized prices, and CAGR. Demonstrate simple returns compound; log returns sum and exp to converge on the same result.

Compare six instruments using mean and standard deviation to assess risk and return, noting Bitcoin's high reward and tail risk and the limits of normal distribution in mean-variance analysis.

Financial returns do not follow a normal distribution, making mean and standard deviation insufficient to capture tail risks. The lecture highlights fat tails, skew, and the need for tail-risk metrics.

Calculate mean daily log returns and their standard deviation to assess risk and reward, then annualize metrics using 252 trading days, noting compound annual growth rate and continuous compounding.

Short selling lets traders profit from falling prices by borrowing and selling stock, then buying it back; the caption contrasts 100→110 long gains with 100→90 short gains.

Understand short selling and short positions in euro-dollar trading, showing how a long euro and a short dollar yield asymmetric simple returns, and why log returns suit short positions better.

Learn how covariance and the correlation coefficient reveal whether assets move together, showing zero, positive, or negative relationships, and visualize the correlation matrix with seaborn heat map for portfolio diversification.

Examine margin trading basics, including collateral, leverage, and the amplified gains and losses. Learn to calculate levered returns using simple returns and avoid misapplying leverage.

Balance return and risk with mean-variance analysis. Compare risk-adjusted metrics such as the swap ratio and drawdown measures, and explore margin trading with leverage and the Kelly criterion.

Analyze the risk–return tradeoff and apply risk-adjusted metrics like mean return over the standard deviation, slope from the origin, and the Sharpe ratio, considering the risk-free asset.

Learn to compare six forex instruments and levered strategies using daily data from 2010 to 2020, including neutral benchmarks and a low volatility approach, with risk-adjusted return as the guide.

Compute reward as the mean of daily log returns, then annualize by trading days, calculate CAGR, and rank instruments by annualized mean to compare performance.

Explore risk-adjusted returns by plotting mean versus standard deviation and computing the Sharpe ratio, comparing six instruments and daily versus annual calculations under a zero risk-free rate.

Explore downside risk and the sarteano ratio, compare standard deviation with downside deviation, and learn how a target minimum return shapes risk-adjusted performance.

Import pandas and matplotlib, load six instrument returns, visualize normalized prices to compare returns and risk against a benchmark while preparing trading days per year, Sharpe and Sortino ratios.

Calculate downside deviation (semi-deviation) by subtracting a target minimum return from daily returns, keeping negative excess returns and setting positives to zero, then square, average, and take the square root.

Learn to calculate the Sortino ratio on daily returns for the U.S. dollar and British pound, using excess over a target rate and downside deviation, then annualize with trading days.

Put together a user-defined function to compute the sarteano ratio across panel series or data frames, using excess returns, downside deviation, and a zero minimum return, then annualize.

Explore tail risk metrics, including the maximum drawdown and Colma ratio, to understand worst-case losses beyond the sharp ratio. Compare drawdown duration to guide risk-adjusted returns in trading.

Get started by importing packages, loading six instruments, computing returns, and inspecting data; compare normalized prices, evaluate variance and swap ratio, and benchmark against a zero-investment line.

Calculate the calmar ratio by dividing the compound annual growth rate by the maximum drawdown to compare risk-adjusted performance across six instruments.

Compute maximum drawdown, colma ratio, and maximum drawdown duration from returns and log returns across six instruments in a panel data frame.

Explore the Kelly criterion, which links mean return and variance to determine optimal leverage, and learn why it should be used with other metrics for stable and reliable profits.

Import pandas and matplotlib, compute log returns for six instruments, visualize normalized prices, convert log to simple returns, and multiply by leverage to form levered returns for backtesting margin trading.

Back-test leverage settings from one to five to find the optimal leverage of 2.35, with a best multiplier of 1.28 and worst-case 0.94, and preview the Kelly criterion.

Explore how the Kelly criterion estimates the optimal leverage using simple returns and variance, demonstrating close approximation to the true value with large, non-normal return datasets.

Explore how leverage shapes reward and risk, compare simple vs. log returns, and use risk-adjusted metrics like the Sharpe ratio and Kelly criterion to guide leverage decisions.

Explore how to define, test, and optimize a trading strategy with performance metrics to balance risk, plus leverage, stop loss, take profit orders, and five-hour test to project sustainable income.