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Owiinoo/
flashmind-arb-v15d

Name: flashmind-arb-v15d
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FlashMind v15f — AI Crypto Arbitrage Bot (Real-Data Calibrated)

PPO-trained reinforcement learning agent for cross-DEX arbitrage on Ethereum/L2s. v15f closes the real-data gap discovered in v15d validation.

What Changed: v15d → v15f

v15d achieved perfect discrimination on synthetic data (0% bad executions, composite 0.408). But on real DEX market data, it collapsed: 41.2% bad executions, composite -0.467.

Root cause: VecNormalize observation statistics were trained on synthetic data distribution. Real market observations fell outside the trained distribution, causing policy misfire.

v15f fix:

Fresh VecNormalize (re-learned obs distribution from real+synthetic mix)
Real market data injected during training (refreshed every 25 episodes)
Fine-tuned from v15d_final weights with lower LR (1e-4 → 3e-6)
Best model selected by real data composite score

Real-Data Improvement (v15d → v15f best)

Metric	v15d (real)	v15f (real)	Delta
Bad Execute Rate	41.2%	1.3%	-39.9pp
Skip Rate	15.2%	50.9%	+35.7pp
Good Execute Rate	43.9%	47.7%	+3.8pp
Composite Score	-0.467	0.603	+1.070
Sharpe Ratio	1.67	5.32	+3.65
Avg PnL	300	274	-26
Active Strategies	10	11	+1

Success Criteria: ALL 6 PASSED

Criterion	Target	Actual	Status
Real bad_exec	< 15%	1.3%	PASS
Real skip_rate	> 30%	50.9%	PASS
Real composite	> 0.0	0.603	PASS
Composite improved	> v15d	0.603	PASS
Bad exec reduced	< v15d	1.3%	PASS
No synthetic regression	comp > 0.30	0.458	PASS

Synthetic Performance (No Regression)

Metric	v15d (synth)	v15f (synth)
Composite	0.408	0.458
Sharpe	5.29	6.55
Bad Execute	0.0%	3.4%

Model Architecture

Algorithm: PPO (Proximal Policy Optimization) with MlpPolicy
Parameters: 384K
Observation: 621-dim (598 market + 21 strategy one-hot + 2 opportunity signal)
Action: Discrete(2) — 0=EXECUTE, 1=SKIP

v15f Training Config

Base model: v15d_final (policy weights transferred)
VecNormalize: FRESH (re-learned from real+synthetic mix)
Real data sources: DeFiLlama (pools/TVL), DexScreener (pairs/liquidity), CoinGecko (prices)
Real data refresh: Every 25 episodes during training
Steps: 200K fine-tuning
LR: 1e-4 → 3e-6 linear decay over 150K steps
Best model: Selected by real data composite (step ~140K)
Training time: 69 minutes
Real market pools used: 9

Files in this Repository

File	Description
`flashmind_ppo_v15f_best.zip`	Best model (real composite 0.603)
`flashmind_ppo_v15f_final.zip`	Final checkpoint (step 200K)
`v15f_vecnormalize.pkl`	VecNormalize stats (real-data calibrated)
`flashmind_ppo_v15d_final.zip`	Previous best (synthetic-only)
`v15d_vecnormalize.pkl`	Previous norm stats (synthetic-only)
`backtest_v15_comprehensive.json`	Full backtest of v15b/v15c/v15d/v15e
`v15f_vs_v15d_comparison.json`	Real-data comparison v15d vs v15f
`training_v15f_results.json`	v15f training metadata

Usage

python

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize

model = PPO.load('flashmind_ppo_v15f_best.zip')
# MUST use v15f_vecnormalize.pkl for proper observation normalization

Training History

Version	Key Change	Real Comp	Synth Comp
v15c	First discrimination	N/A	Failed
v15d	norm_reward=False	-0.467	0.408
v15e	LR decay + early stop	N/A	0.440
v15f	Real-data fine-tuning	+0.603	0.458

License

MIT

Owiinoo/flashmind-arb-v15d