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Chess games pipeline

This is a simple scripting package to pull games from Lichess into a PostGreSQL database.

Requirements

In order to run it, the following Python packages are required:

  • luigi
  • psycopg2
  • pandas
  • python-chess
  • python-lichess

A PostGreSQL server must also be set up properly. The data is written to a table with the format listed in chess_games.sql.

Gathering chess data

To gather data from Lichess, run the following command with the location of chess_pipeline.py in your PYTHONPATH:

luigi --module chess_pipeline CopyGames

By default, the script pulls Blitz games from the last two days for the user thibault. This can be changed by passing in the following arguments:

  • --player, for the chess player
  • --perf-type, for the kind of chess game - bullet, blitz, classical, etc.
  • --since, for since when to pull. This is given in Unix time.
  • --single-day, if only a single day is to be pulled from the API (this is a boolean flag)

The above can also be added to your crontab, as long as your PATH and PYTHONPATH are set up correctly.

The script defaults to writing to a (main) table called chess_games. It also writes to tables called game_clocks, game_evals, and game_moves for the clock information, the computer evaluation of the position, and the moves made in the game, respectively.

In order to access these tables, a section named postgres_cfg in your luigi.cfg file containing the following keys must exist:

  • user, the PostGreSQL user to log in as
  • password, the password for the above user
  • host, the hostname for where the server is running
  • port, the port to which the server is listening
  • database, the database to write to

For greater control over write permissions, the following keys are used whenever data is read from the PostGreSQL server:

  • read_user, the PostGreSQL (read-only) user to log in as
  • read_password, the password for the above user

Optionally, you can also add a lichess_token section with the following key:

  • lichess-token is the lichess API token to be used for faster API calls

Local Stockfish

Some games don't have server-side analyses available for them. To allow analysis of these games, a local stockfish can be used to analyze each position and create an evaluation for it. This requires the stockfish library for conversing with stockfish. Using local analysis is as simple as adding the flag:

  • --local-stockfish

Additionally, you also need a stockfish_cfg section with the keys:

  • depth, the depth at which stockfish will analyze
  • location, the location of the stockfish executable

Depending on the processing power of your machine, you might want to choose a low depth - analyzing all the positions takes a while. Server-side analyses are depth 20.

Attributes

For each chess game:

  • game info (variant)
  • game result
  • game link
  • how the game finished (time forfeit/resignation)
  • player name
  • player color
  • rating diff for player
  • player result
  • game type (time control - bullet, blitz, etc.)
  • datetime played
  • clock start time
  • clock increment
  • in arena or not
  • rated or casual
  • player rating
  • queen exchange or not
  • player castling side
  • opening name
  • opening ECO
  • move list
  • move evaluations (server-side if available, otherwise with stockfish if enabled)
  • clock times per move

and similar columns for opponent.

Newsletter

It's also possible to send out a newsletter with some simple graphs/metrics on how your chess games have been for the week. However, there are a few additional Python package requirements for this:

  • seaborn
  • beautifulSoup4

Additionally, two config sections need to be added:

  • newsletter_cfg, with a sender email under the key sender
  • sendgrid, with an API key under the key apikey

The newsletter can be sent with the following command:

luigi --module newsletter SendNewsletter

with the options:

  • --player, for the player whose games we're going to analyze
  • --receiver, for the email address to send to

Currently, the newsletter sends out win rates (in %) for black/white in each time control category, as well as a rating progression chart by day, along with min/maxes for each day.

Running the test suite

The tests are written using pytest. To run the test suite, run the following from the root directory:

make pytest