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name: ganglion-synth-city description: "Mine Bittensor Subnet 50 (Synth) with Ganglion. Covers price-path simulation, CRPS scoring, volatility estimation, backtesting, and multi-asset forecasting." homepage: https://github.com/TensorLink-AI/ganglion metadata: {"openclaw": {"emoji": "\u26d3", "requires": {"anyBins": ["ganglion", "curl"]}, "always": false}}

Synth City — SN50 Mining Skill

Synth (netuid 50) is a probabilistic price-forecasting subnet on Bittensor. Miners generate 1 000 Monte Carlo price paths per asset. Validators score submissions using CRPS (Continuous Ranked Probability Score) across multiple time increments. Emissions split 50/50 between low-frequency (24 h) and high-frequency (1 h) competitions.

This skill provides SN50 domain knowledge. Use the ganglion skill for generic Ganglion API commands.

Repo: https://github.com/mode-network/synth-subnet/tree/main/synth

How SN50 works

Validator                              Miner
─────────                              ─────
1. SimulationInput ──────────────────► 2. Fetch live price (Pyth Hermes)
   (asset, start_time,                    Generate 1 000 price paths
    time_increment, time_length,
    num_simulations=1000)
                                       3. Return tuple:
                                    ◄──── (timestamp, increment,
                                           [path_1], …, [path_1000])

4. Wait for horizon to elapse
5. Fetch realised prices (Pyth Benchmarks)
6. Compute CRPS per scoring interval (basis points)
7. 90th-percentile cap → normalise (best=0) → asset coeff
   → 10-day rolling avg (5-day half-life) → softmax → weights

Competitions

	Low-frequency	High-frequency
Assets	BTC ETH SOL XAU SPYX NVDAX TSLAX AAPLX GOOGLX	BTC ETH SOL XAU
Horizon	24 h (86 400 s)	1 h (3 600 s)
Increment	5 min (300 s)	1 min (60 s)
Points / path	289	61
Paths	1 000	1 000
Scoring intervals	5 min, 30 min, 3 h, 24 h abs	1 min → 60 min (18 intervals)
Rolling window	10 days (5-day half-life)	3 days
Softmax β	−0.1	−0.2
Emission share	50 %	50 %
Cycle time	60 min	12 min

Submission format

(
    start_timestamp,          # int — unix seconds, must match request
    time_increment,           # int — seconds, must match request
    [p0, p1, …, pT],         # path 1 — T = time_length / time_increment + 1
    [p0, p1, …, pT],         # path 2
    …                        # 1 000 paths total
)
# Max 8 significant digits per price value.
# Validation rejects: wrong timestamp, wrong increment, wrong path count,
# wrong path length, non-numeric values, >8 digits.

CRPS scoring pipeline

1. Price changes computed in basis points: (diff / price) × 10 000
2. Intervals ending in _abs use absolute prices normalised by real_price[-1] × 10 000
3. Intervals ending in _gap use only the first two points in each chunk
4. NaN gaps handled by label_observed_blocks — only consecutive non-NaN blocks scored
5. properscoring.crps_ensemble(real_value, simulated_ensemble) per time step
6. Total CRPS = sum across all intervals and time steps

Post-CRPS normalisation:

1. Cap at 90th percentile; invalid scores (-1) set to p90
2. Shift so best miner = 0
3. Multiply by per-asset coefficient
4. 10-day rolling average with 5-day half-life exponential decay
5. Softmax with negative β → lower CRPS = higher weight
6. Sum low-freq + high-freq weights → set_weights on chain

Per-asset coefficients (weight normalisation)

BTC: 1.000   ETH: 0.672   SOL: 0.588   XAU: 2.262
SPYX: 2.991  NVDAX: 1.389 TSLAX: 1.420 AAPLX: 1.865  GOOGLX: 1.431

XAU and equity coefficients are higher — improvements on those assets have outsized impact on emissions.

Reference sigma values (hourly, GBM baseline)

BTC: 0.00472  ETH: 0.00695  SOL: 0.00782  XAU: 0.00208
SPYX: 0.00156 NVDAX: 0.00342 TSLAX: 0.00332 AAPLX: 0.00250 GOOGLX: 0.00332

These are from synth/miner/simulations.py. Competitive miners should NOT use fixed sigma — estimate from recent data instead.

Price data sources

Source	Used by	URL	Resolution
Pyth Hermes	Miners (live price)	https://hermes.pyth.network/v2/updates/price/latest	Real-time
Pyth Benchmarks	Validator (scoring)	https://benchmarks.pyth.network/v1/shims/tradingview/history	1 min

Benchmarks symbols: Crypto.BTC/USD, Crypto.ETH/USD, Crypto.SOL/USD, Crypto.XAUT/USD, Crypto.SPYX/USD, Crypto.NVDAX/USD, Crypto.TSLAX/USD, Crypto.AAPLX/USD, Crypto.GOOGLX/USD

Tools in this project

Tool	Category	Purpose
run_experiment	training	Generate Monte Carlo paths. Docstring contains full SN50 subnet reference.
fetch_price	data	Live spot price from Pyth Hermes oracle
fetch_historical_prices	data	Historical prices from Pyth Benchmarks (same source validator uses for CRPS scoring). Returns timestamps, prices, returns in bps, gap info.
estimate_volatility	training	Estimate sigma from recent returns (realized vol or EWMA)
score_paths	evaluation	CRPS against realised prices (single window, quick check)
backtest	evaluation	Full validator scoring pipeline replay: multi-interval CRPS at all SN50 scoring increments, per-asset coefficients, NaN handling, percentile normalisation. Supports both low_freq and high_freq competitions.

Search strategies

1. Start with GBM + reference sigma as baseline — establishes a CRPS floor
2. Use fetch_historical_prices to pull recent data, then estimate_volatility with EWMA
3. backtest your paths against the historical window to get exact validator-equivalent CRPS
4. Try GARCH(1,1) for volatility clustering — this is what CRPS rewards
5. Add jump-diffusion (Merton) for sudden price moves GBM misses
6. Regime-switching (bull/bear/sideways) for adaptive volatility
7. Student-t or skewed-t innovations instead of normal (fat tails)
8. Ensemble: blend paths from multiple models (e.g. 500 GBM + 500 jump)
9. Calibrate per-asset: BTC and SOL need different models than XAU or SPYX
10. Target high-coefficient assets first (XAU=2.262, SPYX=2.991) for max emission impact

Known pitfalls

• Constant sigma fails during high-volatility regimes — CRPS will spike
• Overfitting to recent action → narrow distributions that blow up on regime change
• Too few paths (< 500) → noisy CRPS estimates; use 1 000 (the required amount)
• Ignoring 24/7 BTC vol structure (weekends, overnight) degrades scores
• Neural SDE training is unstable with < 90 days of minute-level data
• Same distribution for BTC and ETH ignores different vol characteristics
• Exceeding 8 significant digits per price value → validator rejects submission
• Not matching start_timestamp or time_increment exactly → rejection

Bootstrap workflow

# Scaffold (if using ganglion init)
ganglion init ./synth-city --subnet sn50 --netuid 50

# Or copy this example directory and run:
export GANGLION_PROJECT=./examples/synth-city
ganglion status $GANGLION_PROJECT
ganglion tools $GANGLION_PROJECT
ganglion run $GANGLION_PROJECT

# Remote mode
ganglion serve ./examples/synth-city --bot-id alpha --port 8899
export GANGLION_URL=http://127.0.0.1:8899

Example workflow (remote mode)

# 1. Fetch 48h of BTC history at 5-min intervals
curl -s -X POST "$GANGLION_URL/v1/run/experiment" \
  -H "Content-Type: application/json" \
  -d '{"config": {"tool": "fetch_historical_prices", "asset": "BTC", "hours_back": 48, "time_increment": 300}}' \
  | jq .data

# 2. Run GBM baseline
curl -s -X POST "$GANGLION_URL/v1/run/experiment" \
  -H "Content-Type: application/json" \
  -d '{"config": {"asset": "BTC", "model_type": "gbm", "num_simulations": 1000}}' \
  | jq .data

# 3. Backtest against the historical window
# (pass realized_prices from step 1 into the backtest tool)

# 4. Check knowledge for patterns
curl -s "$GANGLION_URL/v1/knowledge?capability=simulate" | jq .data

Key dependencies

• numpy — path generation, array ops
• properscoring — CRPS calculation (pip install properscoring)
• Pyth Hermes API — live prices
• Pyth Benchmarks API — historical prices (same source as validator)