SlinkyLayer Overview
SlinkyLayer is a purpose-built infrastructure layer for training, validating, and deploying crypto-market AI models. It removes the operational burden of data pipelines and cloud orchestration, allowing users to focus on strategy design and risk settings.
What SlinkyLayer Offers
Managed Training Pipeline • Automatic ingestion of OHLCV data from major exchanges. • Feature library covering technical indicators and custom scalers. • Distributed training jobs with checkpoints, logs, and seed control.
Standardised Action Space Trained agents output one of three discrete instructions at each bar: LONG_100 (open or maintain a full long) SHORT_100 (open or maintain a full short) FLAT_0 (close to cash)
This minimal set is sufficient for directional trading while keeping downstream integration simple.
Transparent Validation • Chronological train-test splits with optional walk-forward folds. • Identical cost and slippage assumptions in training and back-test. • Exported equity curves, trade ledgers, and risk metrics (Sharpe, drawdown, Calmar).
Current Focus: Reinforcement Learning
The first release supports on-policy and off-policy reinforcement algorithms (PPO, A2C, SAC, TD3, DDPG). Hyper-parameter ranges and defaults are pinned, and every run stores its full configuration plus data hashes for perfect reproducibility.
Forward Compatibility
The platform is architected for plug-in model families:
Vision Models
Chart-image detectors for patterns such as breakouts and wedges
Design
Hybrid Signals
Weighted blends of rule logic and AI outputs
Planned
Novel Architectures
Future RL variants or transformer policies
Open
Marketplace and Governance
Users may keep models private or publish them to a shared gallery. Published models enter a tokenised voting process where the community allocates $SLINKY rewards to strategies that show superior, verifiable performance. This encourages transparent competition and makes continuous improvement profitable for creators.
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