# Synth: Synthetic Price Data ## Overview [Synth](https://www.synthdata.co/) is a decentralized prediction subnet on Bittensor that forecasts crypto and financial asset prices through probabilistic modeling. Instead of predicting a single price, participants build models that output probability distributions, capturing not just where an asset might go, but the range of possibilities and their likelihoods. This approach helps model real market behavior like volatility clustering and tail risks that traditional forecasts often miss. In this Crunch, you need to build probabilistic forecasting models for Bitcoin, Ethereum, Solana, Tether Gold, and tokenized stocks (Apple, Nvidia, Tesla, Alphabet, S\&P 500). Your models will predict over 1-hour and 24-hour price distributions scored on statistical accuracy. The best-performing models will be aggregated into an ensemble forecast that captures collective intelligence from our community. ## How to participate Enter Synth and submit full return density forecasts for selected assets. **Trackers (models)** must generate returns density predictions, not single predictions and maximize accuracy overall forecasts. See open-source Crunch framework**:** [Crunch-Synth](https://github.com/crunchdao/crunch-synth) **Covered Assets:** * Bitcoin (BTC) * Ethereum (ETH) * Solana (SOL) * Tether Gold (XAUT) * SP500 tokenized ETF (SPYX) * NVIDIA tokenized stock (NVDAX) * Tesla tokenized stock (TSLAX) * Apple tokenized stock (AAPLX) * Alphabet tokenized stock (GOOGLX) * Ripple (XRP) * Hyperliquid (HYPE) * Crude Oil WTI (WTIOIL) ## Phases * \[Phase 1] - 1-month warmup period before Live Trading * \[Phase 2] - 3 months live with a fixed $30,000 USDC prize pool * \[Phase 3] - Rewards pools will be generated from mining Synth subnet and may be higher. ## Prediction Targets Trackers must predict the **probability distribution of returns**, defined as: $$ r\_{t,k} = P\_t - P\_{t-k} $$ For each defined step $$k$$ (e.g., 5 minutes, 1 hour, …), your tracker must return a full **probability density function (PDF)** over the future price change $$r\_{t,k}$$. ## Visualize the challenge The Synth coordinator is evaluated on **incremental return predictions**, not raw prices.\ Incremental returns capture the *relative* change in price and produce a stationary series that is easier to model and compare across assets. Below is an example of a **density forecast over incremental returns for the next 24h at 5-minute intervals**:
Below is a minimal example showing what your tracker might return: ```python # Expected output >>> model.predict(asset="SOL", horizon=86400, step=300) [ { "step": (k + 1) * step, "prediction": { "type": "builtin", "name": "norm", "params": { "loc": -0.01, # mean return "scale": 0.4 # standard deviation of return } } } for k in range(0, horizon // step) ] ``` Here is the **return forecast mapped into price space**:
### Game Rules #### Start * The game begins with a **model calibration /** **warm-up phase of 4 weeks,** where predictions are scored but **not rewarded.** * Leaderboard is based on **7-day rolling average** of CRPS scores. * A player must wait **7 days** to get a meaningful ranking. * Players may **enter or exit the game at any time**. * Each player may run **two active models**, which can be updated at any time. #### Prediction Phase In each prediction round, players must submit **a set of** **density forecasts**. A **prediction round** is defined by **one asset**, **one forecast horizon** and **one or more step resolutions**. * A **24-hour horizon** forecast * Triggered **every 1 hour** for each asset * Step resolutions: **{5-minute, 1-hour, 6-hour, 24-hour}** * Supported assets: ```python ["BTC", "SOL", "ETH", "XAUT", "SPYX", "NVDAX", "TSLAX", "AAPLX", "GOOGLX", "XRP", "HYPE", "WTIOIL"] ``` * A **1-hour horizon** forecast * Triggered **every 12 minutes** for each asset * Step resolutions: **{1-minute, 5-minute, 15-minute, 30-minute, 1-hour}** * Supported assets: ```python ["BTC", "SOL", "ETH", "XAUT", "HYPE"] ``` All required forecasts for a prediction round must be generated **within 40 seconds**. {% hint style="info" %} Don't submit models that needs more than 40 seconds to infer! {% endhint %} #### Scoring * Once the full horizon has passed, each prediction is scored using a [**CRPS**](https://en.wikipedia.org/wiki/Scoring_rule#:~:text=%5B8%5D-,Continuous%20ranked%20probability%20score,-%5Bedit%5D) **scoring function**. * A lower **CRPS score** reflects more accurate predictions. * Missing or invalid predictions receive the **worst CRPS score of the round**. Leaderboard ranking is based on a **7-day rolling average** of CRPS scores across **all assets and horizons**, evaluated **relative to other participants**, for each prediction round: * The **best CRPS score is assigned a normalized score of 1** * The **worst 5% of CRPS scores are assigned** **a score of 0** * Intermediate scores are scaled accordingly ### Leaderboard & Game State The leaderboard displays several **relative performance indicators**, computed across **all assets and horizons**: * **Primary Metric** * **Anchor CRPS (7 days):** Rolling relative CRPS average over the past 7 days. * **Secondary Metrics** * **Steady CRPS (3 days):** Rolling relative CRPS average over the past 3 days. * **Recent CRPS (24 hours):** Rolling relative CRPS average over the past 24 hours. #### Payouts * $30K over the first 4 months followed by real mining rewards from Synth Miners (currently up to 50K / months) * Rewards are distributed at target resolution + 24h (every 7 + 1 days) * Fixed reward pool is allocated for each payout period. * the top 10 participants receive 100% of the pot. * Only models **outperforming the benchmark model (synth:benchmarktracker)** are rewarded. * Models with a score **below or equal to the benchmark at payout time** are included in calculations but receive no payout, leaving any fraction of the pool tied to lower-performing models undistributed. * The benchmark model is a reference model provided by the game in order to reward community outperformance over internal Benchmark and may evolve over time.

Example of reward distribution when 7 people beat the Benchmark.

In this example, only 7 participants outperform the benchmark and receive approximately 70% of the weekly rewards. The remaining 30% is retained in the rewards pool.

## Probabilistic Forecasting Probabilistic forecasting provides **a distribution of possible future values** rather than a single point estimate, allowing for uncertainty quantification. Instead of predicting only the most likely outcome, it estimates a range of potential outcomes along with their probabilities by outputting a **probability distribution**. A probabilistic forecast models the conditional probability distribution of a future value $$(Y\_t)$$ given past observations $$(\mathcal{H}\_{t-1})$$. This can be expressed as: $$ P(Y\_t \mid \mathcal{H}\_{t-1}) $$ where $$(\mathcal{H}\_{t-1})$$ represents the historical data up to time $$(t-1)$$. Instead of a single prediction $$(\hat{Y}\_t)$$, the model estimates a full probability distribution $$(f(Y\_t \mid \mathcal{H}{t-1}))$$, which can take different parametric forms, such as a Gaussian: $$ Y\_t \mid \mathcal{H}\_{t-1} \sim \mathcal{N}(\mu\_t, \sigma\_t^2) $$ where $$(\mu\_t)$$ is the predicted mean and $$(\sigma\_t^2)$$ represents the uncertainty in the forecast. Probabilistic forecasting can be handled through various approaches, including **variance forecasters**, **quantile forecasters**, **interval forecasters** or **distribution forecasters**, each capturing uncertainty differently. For example, you can try to forecast the target location by a gaussian density function (or a mixture), thus the model output follows the form: ```python { "density": { "type": "builtin", "name": "norm", "params": { "loc": y_mean, "scale": y_var } }, "weight": weight } ``` A **mixture density**, such as the gaussian mixture $$\sum\_{i=1}^{K} w\_i \mathcal{N}(Y\_t | \mu\_i, \sigma\_i^2)$$ allows for capturing multi-modal distributions and approximate more complex distributions.
The meaning of probabilistic forecast is made more precise by means of the Python [density](https://github.com/microprediction/density) package which provides a dict specification of continuous univariate density function mixtures using the pydantic Python package. The function [validatedensitydict](https://github.com/microprediction/density/blob/main/density/validatedensitydict.py) will tell you whether or not your specification is valid. ## Create your Tracker A **tracker** is a model that processes real-time asset data to **predict future price changes**. It uses past prices to generate a **probabilistic forecast** of incremental returns. **You can use the data provided by the challenge or any other datasets to improve your predictions.** It operates incrementally: prices are pushed to the tracker as they arrive and predictions are requested at specific times by the framework. **To create your tracker, you need to define a class that implements the `TrackerBase` interface, which already handles:** * price storage and alignment via `PriceStore` * multi-resolution forecasting through `predict_all()` As a participant, you only need to implement **one method**: `predict()`. **Required method: `predict(self, asset: str, horizon: int, step: int)`** It must return a sequence of **predictive density distributions** for the **incremental price change** of an asset: * Forecast horizon: horizon seconds into the future * Temporal resolution: one density every step seconds * Output length: horizon // step Each density prediction must comply with the [density\_pdf](https://github.com/microprediction/densitypdf/blob/main/densitypdf/__init__.py) specification. {% hint style="info" %} You can refer to the [Tracker examples](https://github.com/crunchdao/crunch-synth/tree/master/crunch_synth/examples) for guidance. {% endhint %} ## Additional Resources * [Literature](https://github.com/crunchdao/crunch-synth/blob/master/LITERATURE.md) * Useful Python [packages](https://github.com/crunchdao/crunch-synth/blob/master/PACKAGES.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.crunchdao.com/real-time-competitions/competitions/synth.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.