MANTIS

Bittensor Subnet 123 — Multi-challenge signal aggregation network.

---

Architecture

Validators sample miner payloads every SAMPLE_EVERY blocks, decrypt them after a timelock maturation window, and store (embedding, price) pairs in a SQLite database. Periodically, walk-forward scoring computes per-hotkey salience for each challenge, aggregates across challenges by weight, applies EMA smoothing, and sets on-chain weights.

graph TD
    subgraph validator["validator.py"]
        A["Sample block"] --> B["cycle.get_miner_payloads()"]
        B --> C["ledger.append_step()"]
        A --> D["Periodic weight calc"]
        D --> E["ledger.iter_challenge_training_data()"]
        E --> F["model.multi_salience()"]
        F --> G["EMA smooth + set_weights()"]
    end

    subgraph ledger["ledger.py (SQLite)"]
        H["challenge_data"]
        I["raw_payloads"]
        J["drand_cache"]
    end

    subgraph external["External"]
        K["Miners (R2)"]
        L["Drand beacon"]
        M["Subtensor"]
        N["price_service.py → R2"]
    end

    C --> H
    C --> I
    E -- reads --> H
    B -- downloads --> K
    I -- decrypt via --> L
    G -- writes --> M
    B -- reads commits --> M
    N -- publishes --> H

Loading

---

Challenges

All challenges are defined in config.py under CHALLENGES. Each specifies a ticker, dim, blocks_ahead (forward horizon in blocks at 12s/block), loss_func (scoring dispatch key), and weight (relative importance in final aggregation).

Challenge	Ticker	dim	Horizon	loss_func	Weight	Description
ETH-1H-BINARY	ETH	2	300 (1h)	binary	1.0	Binary direction prediction
CADUSD-1H-BINARY	CADUSD	2	300	binary	1.0	"
NZDUSD-1H-BINARY	NZDUSD	2	300	binary	1.0	"
CHFUSD-1H-BINARY	CHFUSD	2	300	binary	1.0	"
XAGUSD-1H-BINARY	XAGUSD	2	300	binary	1.0	"
ETH-HITFIRST	ETHHITFIRST	3	500	hitfirst	2.5	Barrier-hit direction
ETH-LBFGS	ETHLBFGS	17	300 (1h)	lbfgs	3.5	Volatility regime + quantile paths
BTC-LBFGS-6H	BTCLBFGS	17	1800 (6h)	lbfgs	2.875	"
MULTI-BREAKOUT	MULTIBREAKOUT	2/asset	event	range_breakout_multi	5.0	Range breakout continuation/reversal (33 assets)
XSEC-RANK	MULTIXSEC	1/asset	1200 (4h)	xsec_rank	3.0	Cross-sectional return ranking (33 assets)
FUNDING-XSEC	FUNDINGXSEC	1/asset	2400 (8h)	funding_xsec	4.0	Cross-sectional funding rate ranking (20 assets)

---

Scoring

Per-challenge scoring

Each loss_func has its own scoring path. All use L2 logistic regression and coefficient-based importance, but the structure differs.

Binary (binary) — Walk-forward with ElasticNet meta-model. Feature selection: per-miner L2 logistic on first half, AUC on second half, select top-(K) (default 50). Meta-model: ElasticNet logistic (L1 ratio 0.5) on OOS base-model predictions across walk-forward segments. Importance = (|\beta_j|). Segments weighted by recency.

LBFGS (lbfgs) — Two independent scoring paths blended 75/25:

• Classifier path (compute_linear_salience): per-class L2 logistic regressions on 5-bucket argmax predictions. Importance = (\beta_j^2) summed across classes. Vectorized balanced accuracy evaluation. Uniqueness penalty suppresses miners with >85% argmax overlap with higher-ranked peers.
• Q-path (compute_q_path_salience): 12 independent binary L2 logistic models (one per tail-bucket / sigma-threshold combination). Importance = averaged (|\beta_j|) across sub-models.

Both paths are individually top-(K) renormalized with exponential rank decay before blending.

HITFIRST (hitfirst) — Two L2 logistic regressions on logit-transformed miner probabilities: one for up-barrier-hit ((y=1) if price hits +(\sigma) first), one for down-barrier-hit. Importance = (|\beta_j^{\text{up}}| + |\beta_j^{\text{down}}|). No walk-forward — single fit on all valid samples.

MULTI-BREAKOUT (range_breakout_multi) — Operates on completed breakout events (not time series). Two-stage: (1) Empirical AUC gate — per-miner AUC on (P_{\text{continuation}}) vs realized label, requiring AUC > 0.5 and ≥ 2 temporal episodes. (2) L2 logistic on z-scored miner predictions with episode-balanced sample weighting (each temporal episode gets equal total weight regardless of event count). Importance = (|\beta_j|).

XSEC-RANK (xsec_rank) — Cross-sectional binary reformulation: label = 1 if asset's forward return exceeds the cross-sectional median. All assets pooled ((N_{\text{assets}} \times) sample multiplier). Walk-forward meta-model: feature selection by per-miner univariate AUC, top-(K) (default 20) selected, L2 logistic meta-model. Importance per segment:

[ w_j = |\beta_j| \cdot \max!\Big(\frac{\text{AUC}_{\text{meta}} - 0.5}{0.5},; 0\Big) ]

Segments aggregated with exponential recency weighting.

FUNDING-XSEC (funding_xsec) — Same structure as XSEC-RANK but on funding rate changes instead of price returns. Embargo = (\max(\text{LAG}, \text{ahead})) with explicit train_cutoff = val_start - ahead to prevent label leakage from forward-looking labels. Stale miners (temporal std < (10^{-4}) per asset column) zeroed before pooling.

Sybil resistance

L2 regularization splits coefficient mass among correlated miners. If (n) clones submit identical predictions, each receives (\approx w/n) weight. L1 (in binary challenges) or the uniqueness penalty (in LBFGS) drives zero-information or duplicate miners to zero.

Weight aggregation

Per-challenge salience vectors are normalized to sum to 1, multiplied by challenge weight, and averaged:

[ s_j = \frac{1}{\sum_c w_c} \sum_c w_c \cdot \hat{s}_{j,c} ]

EMA smoothing ((\alpha = 0.15)) is applied across weight-setting intervals to reduce block-to-block variance. Degenerate distributions (near-uniform or zero-sum) are rejected.

---

Encryption

Dual-path encryption ensures no party can observe predictions before maturation:

1. Owner path — X25519 ECDH + ChaCha20-Poly1305 AEAD. The owner can decrypt immediately for trading.
2. Timelock path — Drand IBE (BLS12-381). After the specified Drand round, validators decrypt via the published beacon signature.

A SHA-256 binding hash over (hotkey, round, owner_pk, ephemeral_pk) is used as AAD, preventing replay, relay, and substitution attacks.

---

Modules

File	Role
config.py	Challenge definitions, network constants, encryption params
validator.py	Block sampling, payload collection, decryption scheduling, weight setting
ledger.py	SQLite storage, submission validation, training data iteration, Drand cache
model.py	multi_salience() — dispatches to per-challenge scoring, aggregates
cycle.py	Miner payload download, commit URL validation
funding_xsec.py	FUNDING-XSEC scoring: forward pairing, label construction, walk-forward
xsec_rank.py	XSEC-RANK scoring
range_breakout.py	MULTI-BREAKOUT state machine + scoring
bucket_forecast.py	LBFGS classifier + Q-path salience
hitfirst.py	HITFIRST barrier-hit scoring
price_service.py	Fetches spot prices (Polygon) + funding rates (OKX/HL/CoinGlass), uploads to R2

---

Storage

SQLite with WAL mode. Tables:

• challenge_data — (ticker, sidx) → price or price_data (JSON for multi-asset), hotkeys (JSON list), embeddings (binary float16 blob)
• challenge_meta — (ticker) → dim, blocks_ahead
• block_index — Sequential index → block number mapping
• raw_payloads — Encrypted ciphertexts held until maturation
• drand_cache — Cached beacon signatures
• breakout_state — Serialized range tracker state

Training data is streamed via generator iteration, not loaded into memory.

---

Key Parameters

Parameter	Value	Location
SAMPLE_EVERY	5 blocks (60s)	config.py
LAG	60 samples	config.py
TASK_INTERVAL	500 blocks	config.py
WEIGHT_CALC_INTERVAL	1000 blocks	config.py
WEIGHT_SET_INTERVAL	360 blocks	config.py
BURN_PCT	0.30 (UID 0)	config.py
MAX_DAYS	60	config.py
EMA alpha	0.15	validator.py
TOP_K (feature selection)	20	funding_xsec.py, xsec_rank.py

---

Payload Format

V2 JSON only. Required fields: v, round, hk, owner_pk, C, W_owner, W_time, binding, alg.

Commit constraints:

• Host: Cloudflare R2 (*.r2.dev or *.r2.cloudflarestorage.com)
• Object key: exactly your hotkey (no path segments)
• Size: ≤ 25 MB

---

Dependencies

Declared in pyproject.toml. Core: bittensor, torch, scikit-learn, numpy, requests, aiohttp, tqdm, boto3.

See MINER_GUIDE.md for submission details per challenge type.

---

License

MIT License (c) 2024 MANTIS