Tao-Quant paper trading · live

Bittensor, TAO, and dTAO subnets — the short version

Bittensor is a decentralized network where independent “subnets” compete to perform useful AI work (inference, training, scraping, audio, bio, etc). Each subnet has its own token (an “alpha” token). TAO is the macro asset that flows between them.

The three layers

Why subnet prices move

• Emissions allocation. The protocol allocates new TAO emissions to subnets weighted by a 30-day half-life EMA of their net stake flow (“Taoflow”). More inflows → more emissions → buying pressure.
• AMM mechanics. Each subnet has a single constant-product pool (TAO ⇄ alpha). A buy of x TAO into a pool with reserve τ produces a 2·x/τ % price change to first order — meaning smaller pools amplify more. This is Maymin (2026) Proposition 1.
• Real-world demand. Validators must hold the subnet’s alpha token to vote on miner quality, so subnets with healthy validator participation have organic floor demand.
• Network effects. A subnet with active miners + validators + rising emissions creates a flywheel; one without these collapses.

How a trade actually works

Why the SMB factor exists

Maymin’s arXiv 2603.29751 paper studied the cross-section of subnet returns and found that a small-minus-big portfolio (long the smallest market-cap subnets, short the largest) earned ~1% per day with a gross Sharpe of 3.84. The mechanism is the AMM: identical TAO emissions cause much bigger % moves in small pools, mechanically.

Glossary

How the bot decides what to trade

Every 30 minutes the bot pulls a live snapshot, runs each subnet through a screener, scores the survivors, builds a target portfolio, and trades the deltas. Each step is intentionally simple so we can attribute outcomes to specific signals.

The full pipeline

1. The screener — drop dust and unhealthy subnets

Out of 129 subnets, we eliminate any that fail any of the following gates:

2. The alphanomics scorer (v4)

Each surviving subnet gets nine component scores in [0,1]. The composite is a weighted sum:

3. Portfolio construction

• Top-K = 6. Six equally-weighted positions per sleeve. Empirically this gives enough diversification without spreading capital so thin that AMM slippage dominates each trade.
• Max 25% per name. Hard cap so a single subnet can never blow up the sleeve.
• Equal weighting, not score-weighted. Score-weighted concentrates in the top name and amplifies single-subnet risk.

4. Execution & friction

• Trade trigger: only when |target − current| > 5% of sleeve equity. Acts as the natural friction filter to avoid over-churning.
• Slippage: min(0.05, (trade_tao / pool_tao) × 0.5) — capped at 5%. Linear approximation of AMM impact.
• Fees: per-pool fee_rate from the API, ~5 bps default.
• Mark-to-market: equity is stamped every tick regardless of whether a trade fires, so we get a continuous P&L curve.

5. The liquidity baseline (control arm)

Same screener, same K=6 equal-weight construction, same execution — but the score is just raw pool liquidity. This is the dumb-money strategy. If alphanomics can’t beat it, the composite score is adding nothing.

6. The DSV Fund picks (curated-allowlist arm)

DSV Fund publishes a list of 20 subnets they have meaningful capital in. We treat that list as a hard allowlist: same screener, same execution, but the score becomes a binary “is it on the list, yes/no” and the portfolio is equal-weighted across whichever members of the 20 currently pass the screen.

• The 20 names: SN2 D-Sperse · SN3 Templar · SN4/51/64 Targon/Llum/Chutes (compute) · SN13 Data Universe · SN17 404-Gen · SN18 Zeus · SN32 ItsAI · SN33 Ready AI · SN34 Bitmind · SN44 Score · SN46 RESI · SN50 Synth · SN56 Gradients · SN62 Ridges · SN68 Metanova · SN75 Hipplus · SN85 Vidalo · SN93 Bitcast.
• Why no algorithmic ranking: the whole point of this arm is to ask “does fund-grade curation beat algorithmic top-K?” Adding a scorer on top would contaminate the test.
• K = 20 (or however many of the 20 pass the screen) — empirically often 8–12 in any given snapshot, so per-name weight lands around 8–12% (well under the 25% cap).

7. SN0 root staking — the “TAO yield” component

Root staking (netuid 0) is fundamentally different from any subnet alpha. There is no AMM pool — TAO staked to the root validator set earns TAO-denominated yield from network emissions. Two of our sleeves (Lewis, Kidd) anchor a large fraction of capital in SN0 because the people behind those allocations view it as the conservative core position: “own TAO with a productive yield, then take risk on top.”

• Position model: SN0 quantity is denominated in TAO. The synthetic price is just the live TAO/USD spot — no pool-impact, no fees, no slippage. A buy of $5,000 at TAO=$240 produces +20.833 TAO; a sell does the inverse.
• Yield accrual: on every tick we compound the SN0 position by (1 + APY)elapsed/year where APY = 8% (a conservative read of recent root validator yield; can be adjusted as a setting).
• Mark-to-market: SN0 notional = qty × tao_usd. So the SN0 sub-line of equity moves with TAO spot and grows ~0.022%/day from accrued yield.
• Why this matters for the test: a sleeve that is 50% SN0 (Lewis) will track TAO+yield closely on its core; the alpha-or-not question only applies to the 50% that's actively rotated through subnet alphas. Same logic at 30% for Kidd.

8. The Lewis allocation (Mark Jeffrey 10x Challenge)

Lewis Smith / Mark Jeffrey publishes their portfolio openly via the jackson-video-resources/bittensor-investing-agent repo. It's a 7-line conservative allocation built on root staking with measured exposure to a small set of revenue-validating subnets. We replicate it as a fixed-weight sleeve — no screener, no scorer.

9. The Kidd allocation (DSV concentrated)

Siam Kidd is CIO of DSV Fund — the world's first TAO-exclusive hedge fund. The 6-line concentrated portfolio reflects a tighter, more aggressive read on which subnets have demonstrated revenue or commercial pull. Less SN0, more concentrated risk per name.

Inception-to-date P&L by sleeve

All numbers are live, pulled from the local SQLite DB at report build time. Each sleeve started with $10,000 paper.

Equity curves

Current open positions — Alphanomics

Current open positions — Liquidity baseline

Current open positions — DSV picks

Current open positions — Lewis (10x Challenge weights)

Current open positions — Kidd (DSV concentrated)

Recent trades (last 8 per sleeve)

What we’re actually trying to learn — and how

Paper trading is cheap. The point is not “simulate making money,” it’s to generate the dataset that lets us decide whether to risk real money — and if so, on which signals.

The hypotheses being tested

How we measure it

Test windows

• Days 1–7: shake-out. Watch for execution bugs, weird snapshot data, edge cases. No conclusions drawn.
• Days 7–14: first IC numbers. Run scripts/ic_analysis.py to compute Spearman correlation between each scoring component and forward returns. Identify components with |IC| > 0.05 and consistent sign.
• Days 14–30: reweight. Drop or down-weight components with no predictive power. Add Taoflow EMA proper (we have enough history at this point).
• Day 30+: regime test. Compare sleeves head-to-head over a full month containing varied TAO macro conditions. Decide: graduate to Phase 2, iterate, or kill.

Why the directive is “more trades, not fewer”

Real-money strategies want to minimize trades to save fees. This system wants to maximize trades to maximize the labeled dataset — every trade is one row of (signal at t, return by t+1). The 5%-of-equity threshold is the natural friction; we don’t artificially gate further.

Risks we’re explicitly tracking

• Survivorship bias. Deregistered subnets disappear from the API. We need a separate snapshot table for failed subnets — currently a TODO.
• Capacity. All findings are valid only at $10K AUM scale. Maymin is explicit that the SMB factor decays sharply with size.
• Regime risk. The first 30 days might land in a strong bull or strong bear for TAO. The 30-day backtest already showed all strategies losing money in the post-Covenant window. We need multi-regime data before any go/no-go.
• Free-tier API. 5 req/min throttle means a tick takes ~25s. Acceptable today; if we add new pull endpoints, recount the budget.
• Single-writer SQLite. Fine at this scale; replace if we ever fan out the runner.