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High-Performance Chess Engine — Built entirely from scratch in Algeria 🇩🇿

"Trained on billions of positions. Defeated every Stockfish level from 1320 to 3070+.
Built from scratch over 6 months of relentless engineering in Algeria."

⬇️ Download · 🐛 Report Bug · 💬 Discussions

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What is FENRIX?

FENRIX is a high-performance UCI chess engine built entirely from scratch in C++.

This is not a port. Not a fork. Not a wrapper.
Every line of code, every weight in the neural network — designed, trained, and engineered by hand.

The engine features a fully custom NNUE neural network, trained from zero using self-play and knowledge distillation against Stockfish across billions of positions on Google Colab — a process that alone took months of iteration, tuning, and compute.

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📊 At a Glance

Language	C++ (full rewrite from Python)
Evaluation	Custom NNUE — trained from zero
Training	Billions of positions, Google Colab, Stockfish as teacher
Protocol	Universal Chess Interface (UCI)
Estimated ELO	~3070+
Gauntlet result	8 wins / 8 games — Beat every Stockfish level
Development time	6 months
Origin	Algeria 🇩🇿

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🏆 Benchmark — Full Gauntlet vs Stockfish

FENRIX was tested in an exhaustive ladder against Stockfish, starting at its weakest and climbing to near-maximum strength. It won every single game.

  FENRIX vs STOCKFISH — Full Gauntlet Results
  ══════════════════════════════════════════════════════════════════

  Round 1 │ Stockfish ~1320 ELO  (Skill Level  0) │  Score: 2/2  ✅ PASS
  Round 2 │ Stockfish ~1820 ELO  (Skill Level  5) │  Score: 2/2  ✅ PASS
  Round 3 │ Stockfish ~2320 ELO  (Skill Level 10) │  Score: 2/2  ✅ PASS
  Round 4 │ Stockfish ~2820 ELO  (Skill Level 16) │  Score: 2/2  ✅ PASS

  ══════════════════════════════════════════════════════════════════
  🏆  CHAMPION — Defeated every Stockfish level in the gauntlet
  📊  Estimated ELO: ~3070+
  ══════════════════════════════════════════════════════════════════

Round	Opponent	ELO	Score	Time	Result
1	Stockfish Skill 0	~1320	2 / 2	~6s avg	✅ WIN
2	Stockfish Skill 5	~1820	2 / 2	~7s avg	✅ WIN
3	Stockfish Skill 10	~2320	2 / 2	~9s avg	✅ WIN
4	Stockfish Skill 16	~2820	2 / 2	~11s avg	✅ WIN
TOTAL	All 4 levels	—	8 / 8	—	🏆 CHAMPION

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📈 ELO Progression — 6 Months, 3 Versions, +1970 ELO

  FENRIX ELO EVOLUTION
  ════════════════════════════════════════════════════════════════════

  Python v1  │  Classical eval (material only)
  ~1100 ELO  │ ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░

  Python v2  │  + NNUE (first generation) + Advanced search
  ~1800 ELO  │ ████████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░

  C++ v3     │  Full rewrite + NNUE retrained from 0 + Colab training
  ~3070 ELO  │ █████████████████████████████████████████████████ 🔥

  ════════════════════════════════════════════════════════════════════
   Baseline        +700 ELO          +1270 ELO          +1970 ELO total
   v1 → v2: Evaluation revolution   v2 → v3: Architecture overhaul

ELO
3200 ┤                                              ▓▓▓  3070+
2800 ┤                                      ▓▓▓▓▓▓▓
2400 ┤                             ▓▓▓▓▓▓▓▓▓
2000 ┤
1800 ┤              ▓▓▓▓▓▓▓▓▓▓▓▓▓
1400 ┤    ▓▓▓▓▓▓▓▓▓
1100 ┤────╯
     └──────────────────────────────────────────────────────
       Month 1    Month 2    Month 3    Month 4    Month 5    Month 6

Version-by-Version Comparison

	v1	v2	v3
Language	Python	Python	C++
ELO	~1100	~1800	~3070+
ELO gain	baseline	+700 (+63%)	+1270 (+70%)
Total gain from v1	—	+700	+1970 (+179%)
Evaluation	Material only	NNUE (gen.1)	NNUE retrained from 0
Training data	None	Limited	Billions of positions
Training method	None	Partial	Colab + Stockfish distillation
Search	Basic alpha-beta	Full search suite	Optimized C++ search
Opening book	None	Polyglot	Polyglot (weighted)

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🧠 Architecture

Neural Network — NNUE (Trained from Zero)

The beating heart of FENRIX. Every weight, every architecture decision — from scratch.

• Custom NNUE architecture designed and implemented in C++
• Trained on billions of positions via Google Colab
• Teacher model: Stockfish (knowledge distillation + self-play)
• Efficient incremental forward pass — board deltas only, not full re-evaluation
• Optimized for sub-millisecond evaluation during search

This is not a fine-tune of an existing network.
It is not a copy of HalfKP or any public NNUE architecture.
It was built and trained by hand, from the first layer to the last weight.

🔍 Search Engine

• Negamax with Alpha–Beta pruning
• Iterative Deepening with aspiration windows
• Null Move Pruning (R=3)
• Late Move Reduction (LMR)
• Killer Move Heuristic
• History Heuristic
• Mate Distance Pruning

🗃️ Transposition Table

• 64-bit Zobrist hashing
• Exact / Lower / Upper bound entries
• Up to ~500,000 entries (configurable)

📖 Opening Book

• Polyglot .bin format
• Weighted move selection for opening diversity

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⚙️ How FENRIX was built — 6 months of work

Phase 1 — Python Prototype (Months 1–2)

The engine started as a Python proof-of-concept. Classical material evaluation, basic alpha-beta search. ELO: ~1100. Slow but functional — a foundation.

Phase 2 — First NNUE & Search Overhaul (Months 2–3)

Added iterative deepening, LMR, null-move pruning, killer heuristic. Integrated a first-generation NNUE network. ELO jumped to ~1800 — a 700-point leap in a single version.

Phase 3 — The C++ Rewrite (Months 3–5)

The entire engine was rewritten in C++ for performance. The NNUE was redesigned and retrained from zero on Google Colab using billions of positions, with Stockfish as the knowledge teacher. This is where the real work was.

Phase 4 — Gauntlet Testing & Optimization (Month 6)

Fine-tuning, time management, pruning depth calibration, memory optimization. Then: the full gauntlet against Stockfish from level 1320 to 2820. 8 wins out of 8 games. ELO estimate: 3070+.

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⏱️ Time Management

Time Available	Search Depth
> 10s	8+
5 – 10s	7
2 – 5s	6
1 – 2s	5
0.5 – 1s	4
< 0.5s	2–3

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🖥️ UCI Protocol

uci                         → engine info + options
isready                     → readiness check
ucinewgame                  → reset internal state
position startpos [moves]   → set position from start
position fen <FEN> [moves]  → set position via FEN
go depth <n>                → search to fixed depth
go movetime <ms>            → search for fixed time
go wtime <ms> btime <ms>    → full time control
go infinite                 → search until "stop"
stop / quit                 → halt / exit

Compatible with Arena, CuteChess, Banksia, and every UCI-compliant GUI.

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📥 Installation

✅ Option 1 — Windows Executable (Recommended)

⚠️ Source code is not public. Only the compiled binary is distributed.

1. Download fenrix.exe from the latest release
2. Add it to your chess GUI of choice
3. Play

No Python. No dependencies. No setup. Drop it in and go.

🎮 GUI Setup — Arena

1. Open Arena → Engines → Install New Engine
2. Select fenrix.exe · Set protocol to UCI
3. Click OK — ready to play

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🔒 License & Source Code

FENRIX is distributed as a compiled binary only.
The source code is proprietary and closed.

This engine represents 6 months of engineering, thousands of training iterations on Google Colab, and countless hours of testing and debugging. The source code will not be made public.

Redistribution, reverse engineering, decompilation, or modification of the binary is strictly prohibited.

See LICENSE for full terms.

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👤 Author

Lecheheb Djaafar — Algeria 🇩🇿
github.com/LechehebDjaafar

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Built with ♟️, C++, and 6 months of relentless dedication — Algeria 🇩🇿

From 1100 ELO to 3070+ — one commit at a time.