RYKER

Optimizing market pattern recognition

NVIDIA H100 • 80GB VRAM

Training on historical market data70.21%

Epochs847/1000

Loss0.0342

Accuracy93.7%

GPU Util98%

Throughput856 it/s

GPU Temp71°C

VRAM Usage65.4/80GB

Batch Size1024

Double Top

94.2%

Bull Flag

92.8%

H&S

91.5%

Cup & Handle

90.7%

Triangle

93.1%

Parameters1.2B

Layers48

Attention32 heads

Context8192 tokens

Data Points1.2M candles processed

1m5m15m1h4h1d

gpu_metrics.log

command_execution.sh

➜ python train.py --model=market_patterns --epochs=1000

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