Disclaimer

Neither the outputs nor source code of Techalyzer nor the views of its contributors constitute professional or financial advice.

Techalyzer

After taking Machine Learning for Trading at Georgia Tech, I wanted to try computational investing/algorithmic trading on my own time, with unrestricted tooling. This repo contains that work.

A few goals:

• Have something I can easily generate trading insights from for fun (and if I'm lucky, profit)
• Be able to use Rust for performance

Design

I want to be able to automatically fetch daily market data (maybe even minute to minute data if possible) from one or more APIs, and then for a given stock:

• Backtest and optimize a trading strategy using several indicators and either a handwritten algorithm or a machine learning model.
• Using a trained model or algorithm, recommend a course of action given new data each day.

This will involve implementing or sourcing:

• A market simulator (how portfolio value changes depending on when you trade a given security)
• Implementing a buy-and-hold benchmark
• Implementations of several technical indicators (at least RSI, BB, MAC, etc)
• One or more ML algorithms well-suited to maximizing portfolio return given technical signals, or an optimizer that can tune the signals for a handwritten trading strategy

Other annoying subproblems that may crop up:

• Dealing with time series data effectively (including gaps introduced by holidays, weekends, etc). The bdays crate may help with that.
• Having a good plotting solution to sanity check the trading bot
  • Currently Techalyzer serializes to JSON via serde_json and then uses matplotlib (see plot_signals.py)

The basic idea:

+-------------+    +------------------+    +--------------------------------+
| Market Data |--->| Trading Strategy |--->| Insights (Buy/Sell/Do Nothing) |
+-------------+    +------------------+    +--------------------------------+
  Finance API        ML/bespoke algo           Informs human trader 

There is no initial intent to make this a highly scalable or distributed architecture, it is simply a CLI app for now, though the library portions of the code can easily be taken apart and deployed behind some sort of API or messaging interface if so desired.

Known Issues

TBD

Things of Interest

• Polars DataFrame looks like a great fit as a competitor to pandas, should the need arise.

Random Backlog

• TODO: consider using/contributing to rusty-backtest
• TODO: grep for #[should_panic] and fix the tests if they are just todos
• TODO: Add a parameters file to help make model and technical indicators easier to configure
• TODO: Immediately backtest a trained model without having to stitch commands together
• TODO: let the user tweak the parameters of the ML model
• TODO: add different ML algorithms/classifiers
• TODO: implement ensemble learning
• TODO: consider what performance gains can be had from switching off of Vec
• TODO: Charts + statistics (sharpe ratio, returns, etc) for the backtester
• TODO: native plotting solution (no more pyplot scripts)
• TODO: documentation needs to be written, then updated, and double checked for rot
• Integration tests to write in techalyzer.rs:
  • TODO: --file file doesn't exist
  • TODO: --file file isn't valid JSON/CSV/etc.
• TODO: Benchmarking
• TODO: If possible, consider optimizing the amount of copying/cloning
• TODO: Fuzzing, if any functions seem appropriate to fuzz
• TODO: Determine if the name Techalyzer is ok (not trademarked/no bad connotations)
• TODO: make the readme and documentation more instructional when the app frontend is more set in stone.
• TODO: ensure that incoming data uses adjusted close, or investigate the means to program it in.
• TODO: actually hold onto high/low/open/close info instead of doing everything using only closing price
• TODO: add capability to process hourly/minute-to-minute data
• TODO: add crypto (requires processing 24/7 data, maybe stop using NaiveDates)
• TODO: server that can maybe push notifications somehow when it finds a good time to buy or sell.
• TODO: Making a PR for this strum issue would make it possible to show the user what supported indicators there are on a failure to match.
• TODO: Using cargo clippy to catch bad practice.

For funsies:

• Integrate a sentiment analyzer for r/wallstreetbets or various investing forums