# 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**

1. **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.
2. **Standardised Action Space**\
   Trained agents output one of three discrete instructions at each bar:\
   \&#xNAN;*LONG\_100* (open or maintain a full long)\
   \&#xNAN;*SHORT\_100* (open or maintain a full short)\
   \&#xNAN;*FLAT\_0* (close to cash)

   This minimal set is sufficient for directional trading while keeping downstream integration simple.
3. **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:

| Roadmap Item        | Description                                                     | Status  |
| ------------------- | --------------------------------------------------------------- | ------- |
| 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.