Tao-Quant paper trading · live
What this system does
Tao-Quant runs a small portfolio of strategies against the live Bittensor market every 30 minutes. Every trade is paper — no real money. Its job is to generate clean, structured data about which signals actually predict subnet returns so we can graduate the winners to real capital later.
How the pieces fit
Three sleeves run in parallel
| Sleeve | What it trades | Why we run it |
|---|---|---|
subnet_alphanomics | Top-6 subnets by composite score | The algorithmic research strategy — selects from the full universe by score. |
subnet_liquidity | Top-6 subnets by pool depth | Dumb baseline — proves whether scoring beats just buying the biggest. |
subnet_dsv | DSV Fund's curated 20-name allowlist, equal weight | Human/fund picks arm — proves whether expert curation beats algorithmic selection. |
liquidity is the dumb-money floor; alphanomics tests whether algorithmic scoring adds value over that floor; dsv tests whether a fund's curated picks beat the algorithm. If alphanomics can't beat liquidity, the composite score is complexity tax. If DSV can't beat alphanomics, the human-picks premise doesn't hold for this universe.
Key facts
- Cadence: 30-minute ticks via Windows Task Scheduler. Snapshots write every tick; trades fire only when a position drifts > 5% of sleeve equity from target.
- Universe: 129 Bittensor subnets, screened down to ~30 eligible names per tick.
- Capital: $10,000 paper per sleeve. Real-money capacity ceiling is around $10K total per Maymin (2026) — see Methodology tab.
- Data: taostats.io free tier (5 req/min, throttled to 12.5s between calls). Coingecko for spot TAO/USD.
- Friction modeled: per-pool fee_rate from API + AMM slippage (
trade_tao / pool_tao × 0.5, capped 5%).
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
| Term | What it is |
|---|---|
TAO | Native asset of the Bittensor network. Roughly analogous to ETH on Ethereum. |
Subnet | A specialized network within Bittensor that does one type of work (e.g. text inference). Each has a number (netuid) and a name. |
Alpha token | A subnet’s own token. Trades against TAO via the subnet’s AMM pool. |
dTAO | The decentralized-TAO upgrade that gave each subnet its own pool and price discovery (vs the prior proportional-emission model). |
Taoflow | The 30-day EMA of net stake flow into a subnet. The protocol uses this to set emissions. |
Validator | An entity that scores miner work and earns emissions. Must stake TAO + hold subnet alpha. |
Miner | An entity that performs the subnet’s actual work (inference, scraping, etc) and earns emissions. |
SMB factor | Small-minus-big — long small market caps, short large. The dominant alpha factor per Maymin. |
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:
| Filter | Threshold | Reason |
|---|---|---|
| Liquidity (raw) | ≥ 1e11 (~100 TAO) | Drops dust pools where price is meaningless. |
| Pool depth ceiling | ≤ 50,000 TAO | Pools above this have negligible AMM amplification on a $10K trade (Maymin/DSV bound). |
| Active validators | ≥ 8 | Network health — too few validators = capture risk. |
| Active miners | ≥ 10 | Real activity — ~50% of subnets have ≤1 miner. |
| 1-week price change | ≥ −30% | No falling-knife catches. |
| Price > 0 | strict | Sanity. |
2. The alphanomics scorer (v4)
Each surviving subnet gets nine component scores in [0,1]. The composite is a weighted sum:
| Component | Weight | What it measures | Source |
|---|---|---|---|
| size_inverse | 0.25 | Percentile rank by −mcap — small wins | Maymin SMB |
| alphanomics_short | 0.15 | 1-day net stake flow percentile | Taoflow proxy |
| alphanomics_med | 0.15 | 7-day net stake flow percentile | Taoflow proxy |
| momentum_1m | 0.15 | 30-day price change, clipped ±50% | Maymin WML30 |
| momentum_1w | 0.10 | 7-day price change, clipped ±20% | Maymin WML7 |
| emission_yield | 0.05 | Daily emission / mcap | Maymin HML_EMIS |
| buy_sell_pressure | 0.05 | 24h buy_vol / total_vol | Order-flow |
| network_health | 0.05 | validators × miners, percentile | Sanity tilt |
| flow_acceleration | 0.05 | 1d flow vs 7d daily-avg | Catalyst |
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_ratefrom 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).
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
| Subnet | Qty | Avg cost (USD) | Notional |
|---|
Current open positions — Liquidity baseline
| Subnet | Qty | Avg cost (USD) | Notional |
|---|
Current open positions — DSV picks
| Subnet | Qty | Avg cost (USD) | Notional |
|---|
Recent trades (last 8 per sleeve)
Alphanomics
| Time (UTC) | Subnet | Side | Qty | Px USD |
|---|
Liquidity
| Time (UTC) | Subnet | Side | Qty | Px USD |
|---|
DSV picks
| Time (UTC) | Subnet | Side | Qty | Px USD |
|---|
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 four hypotheses being tested
| # | Hypothesis | Falsified if… |
|---|---|---|
| H1 | SMB is real on dTAO subnets in the current regime | size_inverse component has near-zero forward IC after 30 days of capture |
| H2 | Net stake flow predicts forward returns (the Taoflow story) | alphanomics_short / _med components have IC ≈ 0 or negative |
| H3 | Composite scoring beats raw liquidity baseline | alphanomics sleeve underperforms liquidity sleeve over a full month |
| H4 | Subnet selection beats just owning TAO | all three subnet sleeves underperform spot TAO total return |
| H5 | Curated fund picks (DSV) beat algorithmic top-K (alphanomics) | DSV sleeve underperforms alphanomics over a full month — would suggest the algorithm captures whatever edge the fund picks have, with no expert premium |
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.pyto 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.