V7P3R Chess Engine

Latest Release: V17.2.0 - November 22, 2025
A UCI-compatible chess engine achieving 50% score vs Stockfish 1% through advanced search optimization and tactical excellence.

🎯 Current Version: V17.2.0 (Performance Optimization)

V17.2.0 focuses on pure performance improvements through non-invasive optimizations while maintaining v17.1's breakthrough tactical strength.

V17.2.0 Key Features

⚡ Performance Optimizations
- O(1) TT two-tier bucket replacement (eliminates sort overhead)
- Unified TT + evaluation cache (single lookup)
- In-place quiescence sorting (no list allocations)
- Pre-allocated move ordering buffers
- UCI debugging enhancements (seldepth, hashfull tracking)
🎯 Maintained Tactical Excellence
- History heuristic (from v17.0)
- Killer moves (2 slots per ply)
- MVV-LVA capture ordering
- Enhanced transposition table (128 generations)
☁️ Cloud Deployment
- Running 24/7 on Google Cloud Platform (e2-medium VM)
- 4GB RAM, 2 vCPUs for future multi-threading
- Professional infrastructure with 99.9% uptime
- ~$24/month operational cost

V17.2.0 Performance Metrics

NPS: ~5,540 nodes/second (baseline testing)
Search Depth: Typically 10-12 in rapid time controls
TT Usage: 39% at depth 4 (efficient memory utilization)
Cache Hit Rate: 18.1% (improved from 13.3%)

🏆 V17.1 BREAKTHROUGH ACHIEVEMENT

V7P3R v17.1 achieved the most significant performance milestone in development history:

Historic Results: 50% vs Stockfish 1%

Tournament: Engine Battle 20251121_2 (360 games, 9 engines)

Rank	Engine	Score	Win %	vs Stockfish 1%
1	Stockfish 1%	72.5/80	90.6%	N/A
2	V7P3R v17.1	71.5/80	89.4%	10.0/20 (50%) ⭐
3	C0BR4 v3.2	62.0/80	77.5%	1.5/10 (15%)
4	SlowMate v3.1	53.5/80	66.9%	0.5/10 (5%)

Head-to-Head: V7P3R v17.1 vs Stockfish 1% (20 games)

As White: 5.5/10 (55%) - First move advantage utilized ✅
As Black: 4.5/10 (45%) - Solid defensive play
Wins: 9 (including 6 checkmate victories)
Draws: 2 (10% draw rate)
Losses: 9
Time Control: Blitz 1+1 (1 minute + 1 second/move)

Performance Analysis

Strengths:

✅ Tactical Middle Games: 57% win rate (30-60 moves)
✅ Quick Tactics: 60% win rate (<30 moves)
✅ Speed Under Pressure: 5,845 NPS in blitz, zero time losses
✅ Opening Flexibility: 83% score in A40 Queen's Pawn Game
✅ Attacking Prowess: Multiple forcing sequences and checkmates

Growth Opportunities:

⚠️ Long Endgames: 29% win rate (60+ moves) - Stockfish won 5 of 7
⚠️ Technical Positions: Can improve conversion of slight advantages

Rating Achievement:

Performance Rating: ~2600-2650 Elo (vs 1% handicapped Stockfish)
Estimated Improvement: +150 Elo over v16.x series
+25% Performance Gain over previous versions vs Stockfish

See full analysis: docs/V17_1_STOCKFISH_BREAKTHROUGH_ANALYSIS.md

📊 Version History

V17.2.0 (November 22, 2025) - Performance Optimization

O(1) TT bucket replacement system
Unified TT + evaluation cache
In-place quiescence sorting
Pre-allocated move ordering buffers
UCI debugging: seldepth, hashfull tracking
Deployed to GCP e2-medium (4GB RAM, 2 vCPUs)
~5,540 NPS performance (slight regression for infrastructure improvements)

V17.1.1 (November 21, 2025) - Lichess Production Hotfix

Fixed time management for Lichess cloud deployment
Restricted to 10+ minute games (600s minimum base time)
Improved matchmaking acceptance rate
Successfully running on GCP e2-micro (1GB RAM)
Achieved 61% win rate in cloud environment

V17.1 (November 21, 2025) - History Heuristic ⭐ BREAKTHROUGH

Historic Achievement: 50% score vs Stockfish 1% (10/20 games)
2nd Place in 9-engine tournament (71.5/80 = 89.4%)
History heuristic with position-aware tracking
Optimized TT aging (128 generations)
Enhanced quiescence depth limits
Most significant performance jump in development history
Rating: ~2600-2650 Elo (vs handicapped Stockfish)

V17.0 (November 20, 2025) - Advanced Search Techniques

Killer move heuristic (2 slots per ply)
MVV-LVA capture ordering
Enhanced move ordering pipeline
Improved tactical vision
Foundation for v17.1 breakthrough

V16.1 (November 19, 2025) - Enhanced Opening + Bug Fix

Deep 52-position opening book (15 moves)
Middlegame nudges: rooks, king safety, pawn structure
Syzygy tablebase support
Fixed critical "no move found" bug

V16.0 (November 2025) - Fresh Start

Combined MaterialOpponent + PositionalOpponent strengths
60% PST + 40% Material evaluation
Pre-search move filtering

V14.0 (October 25, 2025) - Consolidated Performance

Unified bitboard evaluation system
Tactical detection integration
Pawn structure consolidation

V12.6 (October 4, 2025) - Tournament Champion

Engine Battle 20251004: 7.0/10 points (2nd place)
Regression Battle 20251004: 9.0/12 points (1st place)
30x faster evaluation (152ms → 5ms)
High-performance bitboard evaluation

🚀 Installation & Setup

Requirements

Python 3.12+ (3.13 recommended)
python-chess library: pip install python-chess
Optional: Syzygy tablebases for perfect endgames
Cloud Deployment: Docker, Google Cloud SDK (for lichess-bot)

Quick Start

# 1. Clone repository
git clone https://github.com/pssnyder/v7p3r-chess-engine.git
cd v7p3r-chess-engine

# 2. Install dependencies
pip install -r requirements.txt

# 3. Test engine
cd src
python v7p3r_uci.py

# 4. In UCI interface, type:
uci
isready
position startpos
go depth 10

Arena Chess GUI Setup

Install Engine:
- Open Arena Chess GUI
- Go to: Engines → Install New Engine
- Navigate to src/ and select: v7p3r_uci.py
- Engine appears as: V7P3R v17.2.0

Configure Python Path (if needed):

Create V7P3R_v172.bat with your Python path:

@echo off
"C:\Users\YourName\AppData\Local\Programs\Python\Python313\python.exe" v7p3r_uci.py

Recommended Settings:
- Time Control: Rapid 10+5 or Classical 15+10
- Max Depth: 10-12 (v17.x series)
- Hash Size: 256MB

☁️ Lichess Cloud Deployment

V7P3R now includes integrated Lichess bot deployment infrastructure.

Repository Structure

v7p3r-chess-engine/
├── src/                    # Core engine
├── lichess/                # Lichess bot integration ⭐ NEW
│   ├── config.yml          # Bot configuration
│   ├── engines/            # Deployed engine versions
│   ├── game_records/       # Game PGNs
│   └── docs/               # Deployment guides
├── docs/                   # Technical documentation
└── testing/                # Test suites

Current Cloud Status

Instance: v7p3r-production-bot (GCP e2-medium)
Engine Version: V17.2.0
Location: us-central1-a
Uptime: 99.9%
Cost: ~$24/month

Quick Deployment

# Upload new engine version (5 minutes)
cd lichess/engines/V7P3R_v17.2.0
tar -czf ../v17.2.0-src.tar.gz src/
gcloud compute scp ../v17.2.0-src.tar.gz v7p3r-production-bot:/home/patss/ --zone=us-central1-a

# Deploy to container
gcloud compute ssh v7p3r-production-bot --zone=us-central1-a --command="
    sudo docker cp v17.2.0-src.tar.gz v7p3r-production:/tmp/
    sudo docker exec v7p3r-production bash -c 'mv /lichess-bot/engines/v7p3r /lichess-bot/engines/v7p3r.backup'
    sudo docker exec v7p3r-production mkdir -p /lichess-bot/engines/v7p3r
    sudo docker exec v7p3r-production bash -c 'cd /lichess-bot/engines/v7p3r && tar -xzf /tmp/v17.2.0-src.tar.gz --strip-components=1'
    sudo docker restart v7p3r-production
"

Full Guide: lichess/docs/CLOUD_DEPLOYMENT_GUIDE.md

⚙️ UCI Options

Option	Type	Default	Range	Description
MaxDepth	spin	10	1-20	Maximum search depth (V17.x: 10-12 recommended)
TTSize	spin	128	16-2048	Transposition table size in MB
UseHistory	check	true	-	Enable history heuristic (V17.1+)
UseKillers	check	true	-	Enable killer move heuristic (V17.0+)

Configuration Examples

# Tournament settings (V17.x)
setoption name MaxDepth value 12
setoption name TTSize value 256
setoption name UseHistory value true
setoption name UseKillers value true

# Blitz settings
setoption name MaxDepth value 10
setoption name TTSize value 128

🎮 Playing Style

V17.x Strengths

🎯 Tactical Excellence
- Multiple forcing sequences found
- 6 checkmate victories vs Stockfish 1%
- Strong piece coordination
- Excellent at king hunts
⚡ Middle Game Mastery
- 57% win rate in 30-60 move games
- Active piece placement
- Effective attacking play
- Strong calculation in complex positions
🔥 Opening Flexibility
- 52+ positions programmed (15 moves deep)
- 83% score in A40 Queen's Pawn Game
- Comfortable in various systems
- Good pawn structure understanding
⚡ Speed Under Pressure
- 5,845 NPS in blitz time controls
- Zero time losses in 20-game match
- Effective time management
- No timeouts even under pressure

V17.x Characteristics

Target Depth: 10-12 (standard time controls)
Nodes/Second: 5,000-6,000 (position dependent)
Search Features: History heuristic, killer moves, MVV-LVA
Evaluation Speed: ~0.17ms per position
TT Efficiency: 18% hit rate, 39% utilization

Ideal Opponents

Competitive Against: Stockfish 1% (50% score), C0BR4 v3.2 (65% score)
Dominates: All opponent engines (100% score), tactical engines without search depth
Challenging: Full-strength Stockfish, engines with >14 depth search

Known Characteristics

Strength: Tactical middle games, quick tactics (<30 moves)
Growth Area: Long endgames (60+ moves) need improvement
Aggressive: Only 10% draw rate (seeks wins over draws)
Reliable: Zero critical bugs in 20-game Stockfish match

🏗️ Technical Architecture

V17.2.0 Core Systems

V7P3REngine (Main Class)
├── Initialization
│   ├── Opening Book (v7p3r_openings_v161.py - 52 positions)
│   ├── Transposition Table (Two-tier bucket system - V17.2)
│   ├── History Heuristic (Position-aware - V17.1)
│   ├── Killer Moves (2 slots per ply - V17.0)
│   └── Move Ordering Buffers (Pre-allocated - V17.2)
│
├── Search Pipeline
│   ├── get_best_move() - Entry point
│   │   ├── Opening Book Lookup (15 moves deep)
│   │   ├── Iterative Deepening (1→max_depth)
│   │   ├── Time Management (adaptive)
│   │   └── UCI Info Output (depth, seldepth, hashfull)
│   │
│   ├── _search() - Alpha-beta with advanced move ordering
│   │   ├── Transposition Table Probe (O(1) two-tier)
│   │   ├── TT Move (highest priority)
│   │   ├── Killer Moves (2 slots per ply)
│   │   ├── History Heuristic (position-aware)
│   │   ├── MVV-LVA (capture ordering)
│   │   ├── Null Move Pruning (depth ≥3)
│   │   └── Late Move Reduction (non-critical moves)
│   │
│   └── _quiescence_search() - In-place sorting (V17.2)
│       └── Capture-only search to quiet positions
│
└── Evaluation System
    ├── _evaluate_position() - Unified TT cache (V17.2)
    │   ├── PST Score (60% weight)
    │   ├── Material Score (40% weight)
    │   └── Middlegame Bonuses
    │
    └── Transposition Table (V17.2 optimizations)
        ├── Two-tier bucket replacement (O(1))
        ├── Unified evaluation cache
        └── 128-generation aging system

File Structure

v7p3r-chess-engine/
├── src/
│   ├── v7p3r.py                    # Main engine (V17.2.0)
│   ├── v7p3r_uci.py                # UCI protocol interface
│   ├── v7p3r_openings_v161.py      # Opening book (52 positions)
│   ├── v7p3r_fast_evaluator.py     # Fast evaluation module
│   └── v7p3r_bitboard_evaluator.py # Bitboard evaluation
│
├── lichess/                        # Lichess bot integration ⭐
│   ├── config.yml                  # Bot configuration
│   ├── engines/                    # Deployed versions
│   │   ├── V7P3R_v17.1.1/
│   │   └── V7P3R_v17.2.0/
│   ├── game_records/               # PGN archives
│   └── docs/
│       ├── CLOUD_DEPLOYMENT_GUIDE.md
│       └── automation_strategy.md
│
├── testing/
│   ├── test_v17_performance.py     # Performance benchmarks
│   └── test_stockfish_match.py     # Stockfish comparison
│
├── docs/
│   ├── V17_1_STOCKFISH_BREAKTHROUGH_ANALYSIS.md  ⭐
│   ├── V17_1_BREAKTHROUGH_SUMMARY.txt
│   ├── V17_2_PERFORMANCE_PLAN.md
│   └── V7P3R_v18_0_V7P3R_DESC_SYSTEM.md  # Future AI system
│
├── build/                          # PyInstaller builds
├── requirements.txt
└── README.md                       # This file

Code Statistics (V17.2.0)

v7p3r.py: ~1,097 lines (core engine with optimizations)
v7p3r_openings_v161.py: ~400 lines (opening book)
v7p3r_uci.py: ~251 lines (UCI interface)
Total: ~1,750 lines of production code

🔬 Performance Benchmarks

V17.1 Stockfish Match (November 21, 2025)

Metric	Value	Context
Overall Score	10.0/20 (50%)	⭐ Rating parity achieved
As White	5.5/10 (55%)	Effective first-move advantage
As Black	4.5/10 (45%)	Solid defensive play
Wins	9 games (45%)	Including 6 checkmates
Draws	2 games (10%)	Aggressive play style
Losses	9 games (45%)	Mostly long endgames
Average Game	55 moves	Balanced across phases
NPS	5,845	Competitive in blitz
Time Control	Blitz 1+1	Zero time losses

Performance by Game Phase

Phase	Moves	V7P3R Wins	Stockfish Wins	Win Rate
Quick Tactics	<30	3	2	60% ⭐
Middle Game	30-60	4	3	57%
Long Endgames	60+	2	5	29% ⚠️

Best Opening Performance

ECO	Opening	Games	V7P3R Score	Performance
A40	Queen's Pawn Game	3	2.5/3	83% ⭐
A50	Indian Defense	3	2.0/3	67%
C00	French Defense	2	1.5/2	75%
D20	Queen's Gambit Accepted	4	2.0/4	50%

🐛 Known Issues & Status

V17.2.0 Status: ✅ Production Ready

Deployment Status:

✅ Deployed to GCP e2-medium (4GB RAM, 2 vCPUs)
✅ Bot online and accepting challenges
✅ Matchmaking active
✅ UCI debugging metrics functioning

Performance Notes:

~5,540 NPS (slight -5% regression from v17.1 baseline)
Trade-off: Better infrastructure for future optimizations
TT bucket collisions offset sorting elimination gains
Unified cache and UCI enhancements validated

No Critical Bugs: All v17.1 issues resolved

📈 Development Roadmap

V17.3+ (Planned)

Address TT bucket collision overhead
Alternative hashing strategies
Lazy move generation (generator pattern) for +15% NPS
Endgame tablebase integration improvements

V18.0 - DESC System (Future)

Dynamic Effort-Based Search Control (DESC)
AI learns V7P3R's performance characteristics
Adaptive search depth based on position complexity
Neural network for effort prediction (not evaluation)
See: docs/V7P3R_v18_0_V7P3R_DESC_SYSTEM.md

V18.1+ - Multi-Threading

Lazy SMP parallel search
Utilize 2-core e2-medium infrastructure
Expected +80% NPS gain (1.8x speedup)
Maintain tactical accuracy

🏆 Tournament Results

V17.1 (November 2025) - ⭐ BREAKTHROUGH

Engine Battle 20251121_2: 71.5/80 (89.4%) - 2nd place
vs Stockfish 1%: 10.0/20 (50%) - Rating parity ⭐
vs C0BR4 v3.2: 6.5/10 (65%)
vs SlowMate v3.1: 9.5/10 (95%)
vs All Others: 61.5/70 (87.9%) - Dominated field
Performance: Most significant jump in development history

V16.1 (November 2025)

Status: Ready for tournament testing
Target: >50% win rate vs baseline

V12.6 (October 2025) - Previous Champion

Engine Battle 20251004: 7.0/10 points (2nd place)
Regression Battle 20251004: 9.0/12 points (1st place)
Performance: 30x faster than V12.2 baseline

🤝 Contributing

This is a personal development project with integrated Lichess bot deployment.

Testing Help:

Run V17.x in tournaments and report results
Test new versions via Lichess challenges (@v7p3r_bot)
Compare vs other engines (Stockfish levels, C0BR4)
Report UCI compatibility issues
Share interesting game PGNs

Development Areas:

Search optimizations (v17.3+)
DESC system implementation (v18.0)
Multi-threading (v18.1+)
Endgame improvements

Contact: Open issues in the repository or submit pull requests

📜 License

Personal project - educational use encouraged

🙏 Acknowledgments

python-chess library by Niklas Fiekas
Stockfish team for the ultimate benchmark
Arena Chess GUI for tournament testing
Lichess for the bot API and professional infrastructure
Google Cloud Platform for reliable hosting
Chess programming community for evaluation techniques and search algorithms

📚 Documentation

Core Documentation

V17.1 Achievement: docs/V17_1_STOCKFISH_BREAKTHROUGH_ANALYSIS.md ⭐
V17.1 Summary: docs/V17_1_BREAKTHROUGH_SUMMARY.txt
V17.2 Plan: docs/V17_2_PERFORMANCE_PLAN.md
V18.0 DESC: docs/V7P3R_v18_0_V7P3R_DESC_SYSTEM.md

Deployment Documentation

Cloud Guide: lichess/docs/CLOUD_DEPLOYMENT_GUIDE.md
Automation: lichess/docs/automation_strategy.md
Config: lichess/config.yml

Performance Data

Tournament Records: engine-metrics/raw_data/game_records/
Analysis: engine-metrics/raw_data/analysis_results/

V7P3R v17.2.0 Status: ✅ Production Ready & Cloud Deployed
Achievement: 50% vs Stockfish 1% (Rating Parity) ⭐
Infrastructure: Professional cloud deployment with 99.9% uptime
Development Focus: Performance optimization + Future multi-threading + DESC AI system

V16.1 Key Enhancements

🔥 Deep Opening Repertoire (15 moves)
- 52+ positions programmed with center-control focus
- White: Italian Game (Giuoco Piano), Queen's Gambit Declined, King's Indian Attack
- Black: Sicilian Najdorf, King's Indian Defense, French Defense, Caro-Kann
- Smooth transition from opening to middlegame
⚡ Middlegame Transition Nudges
- Rook activity: +20cp (open files), +10cp (semi-open files)
- King safety: +10cp per pawn shield
- Pawn structure: +30cp (passed pawns), -20cp (doubled pawns)
- Intelligent piece placement bonuses
♟️ Syzygy Tablebase Integration
- Perfect 6-piece endgame play (when tablebases available)
- WDL (Win/Draw/Loss) probing for guaranteed optimal moves
- Graceful fallback to heuristic search if tablebases unavailable
🐛 Critical Bug Fix
- Fixed "no move found" bug in drawn positions (K vs K, insufficient material)
- Changed from is_game_over() to specific is_checkmate() / is_stalemate() checks
- This bug caused Arena's "illegal move" error in V16.0

V16.1 Architecture

Core Formula:

Evaluation = (PST × 60%) + (Material × 40%) + Middlegame Bonuses

Three-Phase Excellence:

Opening (Moves 1-15): Deep book with center control theory
Middlegame: Positional bonuses for piece activity, king safety, pawn structure
Endgame (≤6 pieces): Perfect play via Syzygy tablebases (or strong heuristics)

📊 Version History

V16.1 (November 19, 2025) - Enhanced Opening + Bug Fix ✅

Deep 52-position opening book (15 moves deep)
Middlegame nudges: rooks, king safety, pawn structure (+70cp potential)
Syzygy tablebase support for perfect endgames
Fixed critical "no move found" bug (caused V16.0 Arena errors)
All game phases tested and verified

V16.0 (November 2025) - Fresh Start

Combined MaterialOpponent + PositionalOpponent strengths
60% PST + 40% Material evaluation
Pre-search move filtering (never sacrifices material)
Castling preservation (king moves deprioritized)
Tournament tested: 0-2 (revealed opening/endgame weaknesses)

V14.0 (October 25, 2025) - Consolidated Performance

Unified bitboard evaluation system
Tactical detection integration
Pawn structure consolidation
Equivalent performance to V12.6 with cleaner architecture

V12.6 (October 4, 2025) - Tournament Champion ✅

Engine Battle 20251004: 7.0/10 points (2nd place)
Regression Battle 20251004: 9.0/12 points (1st place)
30x faster evaluation (152ms → 5ms)
Clean codebase without nudge system overhead
High-performance bitboard evaluation

🚀 Installation & Setup

Requirements

Python 3.12+ (3.13 recommended)
python-chess library: pip install python-chess
Optional: Syzygy tablebases (3-4-5 piece) for perfect endgames

Quick Start

# 1. Install dependencies
pip install python-chess

# 2. Test engine
cd src
python v7p3r_uci.py

# 3. In UCI interface, type:
uci
isready
position startpos
go depth 6

Arena Chess GUI Setup

Install Engine:
- Open Arena Chess GUI
- Go to: Engines → Install New Engine
- Navigate to src/ and select: v7p3r_uci.py
- Engine appears as: V7P3R v16.1

Configure Python Path (if needed):

Create V7P3R_v161.bat with your Python path:

@echo off
"C:\Users\YourName\AppData\Local\Programs\Python\Python313\python.exe" v7p3r_uci.py

Optional: Configure Syzygy Tablebases:
- Download 3-4-5 piece tablebases from Syzygy Download
- In Arena, set UCI option: setoption name SyzygyPath value C:\path\to\tablebases

⚙️ UCI Options

Option	Type	Default	Range	Description
MaxDepth	spin	6	1-20	Maximum search depth (V16.1: 6-10 recommended)
TTSize	spin	128	16-2048	Transposition table size in MB
SyzygyPath	string	(empty)	-	Path to Syzygy tablebase files (V16.1)

Configuration Examples

# Standard tournament settings
setoption name MaxDepth value 8
setoption name TTSize value 256

# With tablebases for perfect endgames
setoption name SyzygyPath value /path/to/syzygy
setoption name MaxDepth value 10

🎮 Playing Style

V16.1 Strengths

🔥 Opening Mastery
- 52 positions deep (15 moves)
- Center control focus (Italian, QGD, Sicilian Najdorf, KID)
- Smooth book exit transitions
⚡ Middlegame Excellence
- Material safety (never sacrifices without compensation)
- Active piece placement (PST-guided)
- Rook activity detection (+20cp open files)
- King safety awareness (+10cp per shield pawn)
- Pawn structure intelligence (passed pawns, doubled pawns)
♟️ Endgame Mastery
- With Tablebases: Perfect 6-piece play (forced wins)
- Without Tablebases: Strong PST + material heuristics
- King centralization in endgames
- Passed pawn promotion technique
🛡️ Tactical Awareness
- Pre-search move filtering (material safety)
- Quiescence search (tactical stability)
- Mate detection (finds checkmate in 1-2 moves)
- King safety preservation (castling prioritized)

V16.1 Characteristics

Target Depth: 8-10 (standard time controls)
Nodes/Second: 3,000-10,000 (position dependent)
Opening Book Usage: Moves 1-15 (52+ positions)
Evaluation Speed: ~5-10ms per position

Ideal Opponents

Competitive Against: C0BR4 v3.2 (primary target), material-focused engines
Challenging: Super-tactical engines, deep search specialists (>12 depth)
Beats: Engines without opening books, weak endgame play

Known Limitations

Depth Limitation: Targets 8-10 depth (not ultra-deep search)
Tactical Horizon: May miss deep (5+ move) tactical sequences
Time Management: Basic time allocation (can be optimized)
No Neural Networks: Classical evaluation only

🏗️ Technical Architecture

V16.1 Core Systems

V7P3REngine (Main Class)
├── Initialization
│   ├── Opening Book (v7p3r_openings_v161.py - 52 positions)
│   ├── Transposition Table (Zobrist hashing)
│   └── Syzygy Tablebase (optional)
│
├── Search Pipeline
│   ├── get_best_move() - Entry point
│   │   ├── Tablebase Probing (≤6 pieces, perfect play)
│   │   ├── Opening Book Lookup (15 moves deep)
│   │   ├── Iterative Deepening (1→max_depth)
│   │   └── Time Management
│   │
│   ├── _search() - Alpha-beta with pruning
│   │   ├── Transposition Table Probe
│   │   ├── Null Move Pruning (depth ≥3)
│   │   ├── Move Filtering (material safety)
│   │   └── Recursive Minimax
│   │
│   └── _quiescence_search() - Tactical stability
│       └── Capture-only search to quiet positions
│
└── Evaluation System
    ├── _evaluate_position() - Main evaluator
    │   ├── PST Score (60% weight)
    │   ├── Material Score (40% weight)
    │   └── Middlegame Bonuses (V16.1)
    │
    └── _calculate_middlegame_bonuses() - V16.1 Enhancement
        ├── Rook Activity (+20cp open, +10cp semi-open)
        ├── King Safety (+10cp per shield pawn)
        ├── Pawn Structure (+30cp passed, -20cp doubled)
        └── Returns total bonus in centipawns

File Structure

v7p3r-chess-engine/
├── src/
│   ├── v7p3r.py                    # Main engine (V16.1)
│   ├── v7p3r_uci.py                # UCI protocol interface
│   └── v7p3r_openings_v161.py      # Opening book (52 positions)
│
├── testing/
│   ├── test_v161_game_phases.py    # Comprehensive phase testing
│   └── test_no_move_bug.py         # Bug diagnostic tests
│
├── docs/                           # Project documentation
├── build/                          # PyInstaller builds
└── README.md                       # This file

Code Statistics (V16.1)

v7p3r.py: ~870 lines (core engine)
v7p3r_openings_v161.py: ~400 lines (opening book)
v7p3r_uci.py: ~180 lines (UCI interface)
Total: ~1,450 lines of production code

🔬 Performance Benchmarks

V16.1 Test Results (November 19, 2025)

Test Category	Result	Details
Opening Book	✅ Pass	52 positions loaded, all book moves valid
Middlegame Bonuses	✅ Pass	+10 to +30cp applied correctly
Endgame (No TB)	✅ Pass	Strong heuristic play (KR vs K, KQ vs K)
Mate Finding	✅ Pass	Mate in 1 found instantly (29999cp)
Tactical Awareness	⚠️ Partial	Finds forks/checks, depth-limited on deep tactics
Bug Fix	✅ Fixed	"No move found" bug eliminated

Benchmark Positions

Starting Position (depth 6, 1s):

Move: d4
Eval: 0cp (balanced)
Nodes: ~267

Complex Middlegame (depth 2, 0.06s):

Move: Qd3
Eval: +46cp (White advantage)
Rook bonus applied: +0cp (no open files yet)

K vs K Draw (depth 2, 0.005s):

Move: Ke5
Eval: +24cp (centeralization)
Previously failed (V16.0 bug) ✅ Now fixed

🐛 Known Issues & Fixes

V16.1 Bug Fix: "No Move Found"

Issue: V16.0 returned None in drawn-by-insufficient-material positions (K vs K, KN vs K), causing Arena to flag "illegal move."

Root Cause: Used board.is_game_over() which returns True for:

✓ Checkmate/Stalemate (correct)
✗ Insufficient material draws (incorrect - still have moves)
✗ 50-move rule (incorrect - still have moves)

Fix Applied:

# Before (buggy):
if board.is_game_over():
    return None

# After (fixed):
if board.is_checkmate():
    return -MATE_SCORE + ply, None
if board.is_stalemate():
    return 0, None
# Continue searching in all other cases

Validation: ✅ All drawn positions now return legal moves

📈 Development Roadmap

V16.2 (Planned)

Enhanced time management (increment handling)
Opening book expansion (100+ positions)
Middlegame bonus tuning (match testing)
Performance profiling and optimization

V17.0 (Future)

Neural network evaluation exploration
Deep tactical search extensions
Advanced endgame heuristics (7-piece positions)
Multi-threading support

🏆 Tournament Results

V16.1 (Testing Phase)

Status: Ready for tournament testing
Target Opponent: C0BR4 v3.2
Expected Performance: >50% win rate vs baseline

V16.0 (November 2025)

Record: 0-2 (vs V14.1, V12.6)
Issue: Arena flagged "illegal move" (now fixed in V16.1)
Analysis: Weak opening book (2 positions), no endgame tablebases

V12.6 (October 2025) - Champion

Engine Battle 20251004: 7.0/10 points (2nd place)
Regression Battle 20251004: 9.0/12 points (1st place vs all V7P3R versions)
Performance: 30x faster than V12.2 baseline

🤝 Contributing

This is a personal development project, but feedback and testing are welcome!

Testing Help:

Run V16.1 in tournaments and report results
Compare vs other engines (C0BR4, Stockfish levels)
Report any UCI compatibility issues
Share interesting game PGNs

Contact: Open issues in the repository or submit pull requests

📜 License

Personal project - educational use encouraged

🙏 Acknowledgments

python-chess library by Niklas Fiekas
Syzygy Tablebases by Ronald de Man
Arena Chess GUI for tournament testing
Chess programming community for evaluation techniques

V16.1 Status: ✅ Production Ready
Primary Target: Beat C0BR4 v3.2
Development Focus: Opening mastery + Perfect endgames + Material safety

Name		Name	Last commit message	Last commit date
Latest commit History 540 Commits
.github/instructions		.github/instructions
analytics		analytics
docs		docs
lichess		lichess
src		src
testing		testing
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
README.md		README.md
V7P3R_v18_current.bat		V7P3R_v18_current.bat
deployment_log.json		deployment_log.json
profiling_results.txt		profiling_results.txt
pyrightconfig.json		pyrightconfig.json
requirements.txt		requirements.txt
test_results.txt		test_results.txt

pssnyder/v7p3r-chess-engine

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V7P3R Chess Engine

🎯 Current Version: V17.2.0 (Performance Optimization)

V17.2.0 Key Features

V17.2.0 Performance Metrics

🏆 V17.1 BREAKTHROUGH ACHIEVEMENT

Historic Results: 50% vs Stockfish 1%

Performance Analysis

📊 Version History

V17.2.0 (November 22, 2025) - Performance Optimization

V17.1.1 (November 21, 2025) - Lichess Production Hotfix

V17.1 (November 21, 2025) - History Heuristic ⭐ BREAKTHROUGH

V17.0 (November 20, 2025) - Advanced Search Techniques

V16.1 (November 19, 2025) - Enhanced Opening + Bug Fix

V16.0 (November 2025) - Fresh Start

V14.0 (October 25, 2025) - Consolidated Performance

V12.6 (October 4, 2025) - Tournament Champion

🚀 Installation & Setup

Requirements

Quick Start

Arena Chess GUI Setup

☁️ Lichess Cloud Deployment

Repository Structure

Current Cloud Status

Quick Deployment

⚙️ UCI Options

Configuration Examples

🎮 Playing Style

V17.x Strengths

V17.x Characteristics

Ideal Opponents

Known Characteristics

🏗️ Technical Architecture

V17.2.0 Core Systems

File Structure

Code Statistics (V17.2.0)

🔬 Performance Benchmarks

V17.1 Stockfish Match (November 21, 2025)

Performance by Game Phase

Best Opening Performance

🐛 Known Issues & Status

V17.2.0 Status: ✅ Production Ready

📈 Development Roadmap

V17.3+ (Planned)

V18.0 - DESC System (Future)

V18.1+ - Multi-Threading

🏆 Tournament Results

V17.1 (November 2025) - ⭐ BREAKTHROUGH

V16.1 (November 2025)

V12.6 (October 2025) - Previous Champion

🤝 Contributing

📜 License

🙏 Acknowledgments

📚 Documentation

Core Documentation

Deployment Documentation

Performance Data

V16.1 Key Enhancements

V16.1 Architecture

📊 Version History

V16.1 (November 19, 2025) - Enhanced Opening + Bug Fix ✅

V16.0 (November 2025) - Fresh Start

V14.0 (October 25, 2025) - Consolidated Performance

V12.6 (October 4, 2025) - Tournament Champion ✅

🚀 Installation & Setup

Requirements

Quick Start

Arena Chess GUI Setup

⚙️ UCI Options

Configuration Examples

🎮 Playing Style

V16.1 Strengths

V16.1 Characteristics

Ideal Opponents

Known Limitations

🏗️ Technical Architecture