Algorithmic Trading: Backtest, Optimize & Automate in Python

Automate trading to expand monitoring scope and speed, reduce emotional bias, and enable systematic risk management, backtesting, and optimization with historical data.

Explore the pits of automated trading, including overfitting, datasets, and bugs, and learn to monitor and apply logical safeguards to avoid losses.

Explore how automated trading dominates markets, with about 10 percent of trades from human stock pickers. See how execution systems support decisions while humans design and backtest strategies.

Download and deploy the ready-to-use virtual machine from Google Drive to access all course code. Import the appliance in VirtualBox, adjust RAM and settings, and change the default password.

Learn to spot and avoid scammers impersonating me on Instagram and YouTube by checking official account handles, never share phone numbers or Bitcoin, and report impostors.

Install apps on ubuntu using the ubuntu software center and admin rights. Install Telegram desktop and the Aton editor, then edit text with nano to manage files in the terminal.

Explore strings and integers in Python by assigning variables with =, comparing with ==, and checking types with type(); learn valid variable names, string quotes, and dot/leading digit restrictions.

Explore lists and dictionaries in Python, learning indexing, appending, and handling last elements with negative indexing, while using practical examples like fruit, vegetable, and meat to store data in scripts.

Explore Python imports, including packages and modules, use the time and string modules with sleep, and build a Caesar cipher project to encrypt and decrypt messages.

Explore Python functions, def syntax, indentation, and returns to create modular, reusable code; practice with date and weekday and a perfect-number exercise using divisors.

Install git, clone the Montreal Trading Group freetrade repository on GitHub, and explore open source bot code to set up a Telegram driven crypto trading bot.

discover the updated social media handles for the bloom company (replacing mgg) and follow the instructor on the new profiles listed on the site.

Backtest the strategy using 40 days of data, review results, and plot buy-sell signals with the plotting framework, while exploring parameter optimization for Bollinger bands and RSI.

Learn how to optimize trading parameters using the Sharpe ratio to balance returns and risk, and apply backtesting, RSI, Bollinger bands, and Hyperopt to find effective strategies.

Run python3 optimization with a config file to test thousands of parameter combinations, refine Sharpe ratio, disable RSI entry, and use Bohlinger band-based time-based exits with a stop loss.

Backtest the optimization results by applying the given parameters (RSI, Bollinger bands, stop loss) and verify the backtest yields the same returns, ensuring code accuracy before deployment.

Run your strategy in sandbox mode to simulate live market data with fake orders and test against out-of-sample data before live trading.

Perform a walk-forward analysis by splitting data into in-sample and out-of-sample windows, optimize on two-thirds, test on the remaining one-third, and backtest via time-range selection.