Moonfish Engine (~2000 Elo Rating Lichess.org)

Moonfish is a didactic Python chess engine designed to showcase parallel search algorithms and modern chess programming techniques. Built with code readability as a priority, Moonfish makes advanced concepts easily accessible providing a more approachable alternative to cpp engines.

The engine achieves approximately ~2000 Elo when playing against Lichess Stockfish bots (beats level 5 and loses to level 6) and includes comprehensive test suites including the Bratko-Kopec tactical test positions.

Quickstart

Requirements

• Python 3.10

Installation and usage

Install the python library: shell pip install moonfish

From python: ```python $ python

import chess import moonfish board = chess.Board() moonfish.searchmove(board) Move.fromuci('g1f3') ```

You can also call the CLI, the CLI works as an UCI Compatible Engine: shell $ moonfish --mode=uci uci # <- user input id name Moonfish id author luccabb uciok

You can also run it as an API: shell moonfish --mode=api

Then send a request: shell $ curl "http://localhost:5000/?fen=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR%20w%20KQkq%20-%200%201&depth=4&quiescence_search_depth=3&null_move=True&null_move_r=2&algorithm=alpha_beta" { "body": { "move": "e2e4" }, "headers": { "Access-Control-Allow-Headers": "Content-Type", "Access-Control-Allow-Methods": "OPTIONS,GET", "Access-Control-Allow-Origin": "*" }, "statusCode": 200 }

Features

Search Algorithms

• Alpha-Beta Pruning - Negamax with α-β cutoffs
• Lazy SMP - Shared memory parallel search utilizing all CPU cores
  
• Layer-based Parallelization - Distributing work at specific search depths
• Null Move Pruning - Skip moves to detect zugzwang positions
• Quiescence Search - Extended search for tactical positions

Evaluation & Optimization

• PeSTO Evaluation - Piece-square tables (PST) with tapered evaluation. Using Rofchade's PST.
• Transposition Tables - Caching to avoid redundant calculations
• Move Ordering - MVV-LVA (Most Valuable Victim - Least Valuable Attacker)
• Syzygy Tablebase support for perfect endgame play
• Opening Book integration (Cerebellum format)

Engine Interfaces

• UCI Protocol - Compatible with popular chess GUIs
• Web API - RESTful interface for online integration
• Lichess Bot - Ready for deployment on Lichess.org

Configuration Options

| Parameter | Description | Default | Options | |-----------|-------------|---------|---------| | --mode | Engine Mode | uci | uci, api | | --algorithm | Search algorithm | alpha_beta | alpha_beta, lazy_smp, parallel_alpha_beta_layer_1 | | --depth | Search depth | 3 | 1-N | | --null-move | Whether to use null move pruning | False | True, False | | --null-mov-r | Null move reduction factor | 2 | 1-N | | --quiescence-search-depth | Max depth of quiescence search | 3 | 1-N | | --syzygy-path | Tablebase directory | None | Valid path |

Contributing

We welcome contributions, feel free to open PRs/Issues! Areas of interest: - New search algorithms - Improved evaluation functions - Time constrained search (e.g. find the best move in 40s) - Additional test positions - Github CI testing - Different evaluation functions - Neural Net integration - Performance benchmarking on different hardware - Improving caching

References

• Chess Programming Wiki
• python-chess library
• Lazy SMP Algorithm
• UCI Protocol Specification
• Rofchade
• THE BRATKO-KOPEC TEST RECALIBRATED

License

MIT License - see LICENSE file for details.