FAQs
General
Q : What exactly is SlinkyLayer? A : SlinkyLayer is an infrastructure platform for training, validating, and serving live AI models that trade crypto markets. A smart-contract registry anchors model metadata, stakes, audit proofs, and reward emissions.
Q : Is SlinkyLayer a broker or exchange? A : No. The platform emits signed trading signals. Executing those signals is left to your own bot, vault, or desk.
Q : Model vs Policy – why two words? A : Every training run produces a model in the broad sense of “learned weights.” When the algorithm is reinforcement learning, that model is more specifically a policy - a mapping from market state directly to an action, without an intermediate price forecast. All policies are models, but future releases will also host non-policy models (e.g., vision encoders). So we use model for user-facing language and policy when we need the exact technical term.
Q : Can I train computer-vision models such as YOLO today? A : Not yet. The current release focuses on reinforcement-learning policies. The pipeline is model-agnostic, so vision or hybrid models are on the roadmap.
Q : What is Slinky Consensus? A : It is the on-chain mechanism that ranks published models and releases new $SLINKY. A model’s score equals its 7-day forward PnL multiplied by a logarithmic stake term. Rewards are split fifty-fifty between the creator and the stakers who backed the model.
Q : Do I have to publish my model? A : No. Private runs stay in your encrypted storage, produce signals only for your API key, and never touch the chain.
Model Training
Q : Which learning algorithms are available? A : PPO, A2C, SAC, TD3, and DDPG, each with a configurable MLP backbone (layer sizes and activation: Tanh or ReLU).
Q : How large should my training window be? A : The wizard presets cover common horizons. Example: a 60-minute chart trains on 24 months and tests on the next 24 months. Custom dates are allowed.
Q : What is a timestep? A : One environment step equals one candle at the chosen timeframe. Total timesteps is the budget of environment steps the learner will sample before stopping.
Signals
Q : What actions can a model output?
A : Exactly 3 tokens: LONG_100, SHORT_100, and FLAT_0
Q : Which transports are supported? A : WebSocket, HTTPS webhook, Kafka, and a Historical REST API. All channels send the same signed JSON.
Q : What latency should I expect? A : Gateway publishes within two seconds of candle close. Auditors flag signals that arrive after five seconds.
Marketplace and Staking
Q : How is the Slinky Score calculated? A : Detailed explanation is provided here ->
Q : How often are rewards paid? A : Every seven-day epoch the protocol mints new $SLINKY. For any public model, 50 % goes to the creator, 50% to stakers pro-rata by stake-days.
Q : Why use a logarithm on stake? A :The log term gives diminishing returns, preventing a single large holder from dominating rankings while still rewarding broad community support.
Audit and Security
Q : What stops someone from replaying tomorrow’s prices? A : Auditor nodes compare each signal’s timestamp to reference candle close timestamps and compute a segment hash. A segment scores only after three identical hashes reach quorum.
Q : Can I verify a signal myself? A : Yes. Fetch the creator’s on-chain public key, then verify the Ed25519 signature over the raw JSON payload. Recalculate reward with a public price feed to match the auditor math.
Q : What if two models look identical? A : On publish the weights are hashed. If that hash already exists the transaction reverts, preventing duplicate uploads.
Last updated