Daniel Sapkota
293 lines
15 KiB
Markdown
293 lines
15 KiB
Markdown
# 04 — Strategy v2: Maker-Only Quoting for Political Markets
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The v1 strategy is penny-jumping with an after-the-fact stop loss. v2 replaces
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it with a quoting engine built around four ideas:
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1. a **fair value estimate** derived purely from market microstructure (no
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external data in this phase — see 07 for the future feed layer),
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2. **inventory-skewed, volatility-scaled quotes** that never cross the spread,
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3. **liquidity rewards as an explicit term in the objective**, not a boolean
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filter,
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4. **regime detection** that decides when *not* to quote — which is where most
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of the money is saved.
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Strategy code is a pure function `(BookState, Inventory, Params, Clock) ->
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TargetQuotes`, unit-testable and replayable (see §9).
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## 0. The market we're fitting
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Political markets on Polymarket have a specific microstructure profile:
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- **Binary, bounded [0,1]**, usually inside **neg-risk event groups** (multi-
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candidate events where Σ YES = 1 by construction).
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- **Long-dated with punctuated equilibrium**: weeks of low-vol mean-reversion
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interrupted by violent news repricing (debates, indictments, dropouts,
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election nights). The flow between events is heavily retail/directional
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(favorable for a maker); the flow *during* events is fast and toxic.
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- **Early-resolution risk**: a candidate dropping out gates a market to 0
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instantly. There is no external feed to warn us in v2 — microstructure
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(sweeps, book evaporation) is the alarm, and size caps are the survival
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mechanism.
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- Depth is deep at the touch on flagship markets, thin on down-ballot ones —
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parameters must scale with observed book depth, not fixed constants.
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Everything below is designed against this profile.
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## 1. Fair value (FV) engine
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All inputs are local: the book we maintain from the WS and the trade tape.
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- **Microprice**: depth-weighted mid over the top K levels (K≈3), i.e. mid
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pulled toward the thin side. Robust to size-1 dust orders via per-level
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minimum-size filtering (keep v1's `find_best_price_with_size` idea, tuned by
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observed depth instead of hardcoded 100/20 shares).
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- **Flow adjustment**: EWMA of signed aggressor volume (from `last_trade_price`
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/trade events) over ~1–5 min nudges FV in the direction of persistent flow.
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Political markets trend on news; leaning with flow reduces adverse fills.
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- **Cross-outcome consistency (neg-risk)**: Σ of YES microprices across the
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event should ≈ 1. Persistent deviation → books dislocated (news repricing in
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progress) → contributes to regime score (§5); large deviation is also the
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trigger for the optional arb module (§7).
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- **Clamping**: FV is smoothed (short EWMA) and clamped against jumps unless
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the jump is confirmed by trades, not just quote flicker.
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Explicitly *not* an input in v2: polls, news, other venues (see 07).
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## 2. Volatility & toxicity engine
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Replaces the spreadsheet's hour-stale "3_hour volatility" with live estimates:
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- **Realized vol**: EWMA of squared FV returns at three horizons (~10s, ~1m,
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~15m), computed on the WS stream. σ_short drives spread width; the
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short/long ratio drives regime detection.
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- **Sweep detector**: single aggression that consumes ≥N levels or moves FV by
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≥X ticks → immediate event flag (§5).
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- **Markout tracker** (self-tuning core): for every one of our fills, record
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FV at +30s and +5m. Per-market EWMA of markout = realized adverse-selection
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cost. Consistently negative markouts automatically widen that market's
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spread / reduce its size via a toxicity multiplier. This is how the bot
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learns "this market eats me" without any human or external data.
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## 3. Quote construction (maker-only)
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### 3.1 Reservation price and half-spread
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```
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skew = γ · σ_short · (q / q_max) # in price units
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r = FV − skew # reservation price
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δ = max(δ_min, c_vol·σ_short + c_tox·toxicity)
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bid_raw = r − δ ; ask_raw = r + δ
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```
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- `q` = net inventory in the YES token (NO inventory counts negative),
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`q_max` = per-market cap. Long inventory pushes both quotes down: buy
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cheaper, sell more eagerly. This single mechanism replaces v1's pile of
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size-gating special cases.
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- Fees: on CLOB V2 **makers pay zero** — no maker fee term in δ. Taker fees
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(politics 1.00%, per-market `feeSchedule` from the catalog, 03) matter to
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us twice: they fund the **maker rebates** income term (§4), and they enter
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the cost model of the optional set-arb module (§7) only.
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- All prices then snap to tick and go through **placement logic**:
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### 3.2 Placement: join, don't jump
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- Default: place at the better of (raw price, one tick behind the touch) —
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i.e. **join the level or sit behind it**; jump the queue (improve the touch)
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only when `FV − best_ask` / `best_bid − FV` edge exceeds tick + costs. v1
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penny-jumps unconditionally, which wins queue position precisely when the
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price is about to be wrong.
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- **Never cross**: candidate bid ≥ best_ask ⇒ reprice to best_ask − tick (and
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symmetric for asks), validated against the live book at send time. Enforced
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at the exchange too: **every quote is a Post-Only order** (added to CLOB
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Jan 2026, GTC/GTD — 03), which rejects rather than takes. Maker-only is a
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property of the system, not a convention.
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- **Min edge vs FV**: bid ≤ FV − δ_edge_min always; we never pay through fair
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value for queue position or rewards.
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- **Layering**: optionally split size across 2–3 levels per side (touch-join +
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deeper levels). Sweeps fill the deep levels at prices that already include
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the move; layered in-band size also raises the reward score (§4).
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- **Churn control**: reprice only if |target − live| > reprice_ticks or size
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drift > 15%; batch cancel+place; respect the rate budget (03). Queue
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position is an asset — don't burn it for sub-tick prettiness.
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### 3.3 Two-token quoting and the merge loop
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On Polymarket, BUY NO @ q is economically SELL YES @ 1−q, but it is
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collateralized by USDC rather than by YES inventory. v2 makes the **BUY-YES +
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BUY-NO pair the canonical two-sided quote**:
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- Both quotes are bids (USDC-collateralized) → we can quote two-sided with
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zero inventory, from day one, on both sides of the reward band.
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- When both fill: hold YES @ p and NO @ q with p + q < 1 → **merge** pair back
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to 1 USDC. Realized edge = 1 − p − q, captured without ever selling. The
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merge (native Python, 03) runs opportunistically in the background.
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- When inventory accumulates on one leg, exits are dual-routed, still maker:
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(a) keep/raise the opposite-token bid (merge exit), or
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(b) place a SELL limit on the held token above FV.
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The quoter maintains whichever route has the better expected time-to-fill ×
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price; often both, sized to not double-exit.
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### 3.4 Sizing
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- Base size per market from config; scaled down by toxicity, by inventory
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utilization (q/q_max), and near end-date (§6).
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- Respect `orderMinSize`; low-price multiplier logic from v1 (more shares when
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price < 0.10) is replaced by sizing in **USDC notional**, constant per quote
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regardless of price level.
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- No hardcoded 0.1–0.9 price band: quote anywhere the reward band and risk
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checks allow, but size shrinks near the boundaries where payoff asymmetry is
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extreme (long-shot bias zone; near-certain markets pay ~nothing to make).
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## 4. Rewards and rebates as an objective
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Two live income programs (03 §3), both maker-side, both enter the objective:
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- **Liquidity rewards** (same family v1 targeted, confirmed live 2026):
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per-market daily rate, sampled ~per minute; score `S = ((v − s)/v)²` per
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order (s = distance from the dust-filtered *adjusted midpoint*, v =
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max_spread), min-size gate, two-sided rule
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`Q_min = max(min(Q_one, Q_two), max(Q_one/c, Q_two/c))` with c = 3.0, and —
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important — **when mid < 0.10 or > 0.90 only double-sided liquidity scores
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at all**. The formula already lives in v1's scanner (`add_formula_params`);
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v2 moves it into the strategy as a live utility term.
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- **Maker rebates** (new Jan 2026): ~25% of taker fees collected in politics
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markets is redistributed daily to that market's makers pro-rata. Unlike
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rewards (paid for *resting* in-band), rebates are paid for *being filled* —
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they directly subsidize adverse selection and shift the optimizer toward
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tighter quoting in fee-enabled markets with heavy taker flow.
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```
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utility(placement) = E[spread capture] + E[reward $/day at this placement]
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+ E[rebate | fill] − E[adverse selection | regime]
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− inventory penalty
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```
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Consequences the optimizer discovers on its own:
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- **Quiet regime** → sit deeper inside the band with more size (reward-dense,
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low fill probability, low AS cost) — "farming posture".
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- **Hot regime** → rewards can't compensate expected AS loss → widen beyond
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the band or pull entirely. v1 could never do this; its reward check was a
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one-way "must be inside band" constraint that *forced* it to quote tight
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exactly when it shouldn't.
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- **Two-sidedness**: the Qmin rule (and the hard double-sided requirement at
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extreme mids) pays for balanced quoting — another reason the
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BUY-YES/BUY-NO pair posture (§3.3) is the default; it keeps both sides
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in-band even while carrying inventory.
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- Income accounting: actual reward + rebate payouts pulled daily (03) and
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attributed per market, so the utility model's estimates are calibrated
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against reality instead of drifting.
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## 5. Regime detection (when not to quote)
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Per-market state machine, evaluated on every quoter wake:
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| Regime | Trigger (any) | Behavior |
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|---|---|---|
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| QUIET | default | farming posture: in-band, layered, full size |
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| TRENDING | flow EWMA persistent one-sided; σ_short/σ_long elevated | lean FV with flow, widen δ, half size |
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| EVENT | sweep detector; FV jump > J ticks; neg-risk Σ dislocation; book depth evaporates | **pull all quotes**, cooloff T_c (~30–120s), re-enter at 2–3× δ decaying back to normal |
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| REDUCE_ONLY | inventory ≥ hard cap; end-date proximity; risk manager order | exit quotes only |
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| HALTED | WS stale > T; market flagged closed/`acceptingOrders=false`; kill switch | cancel all, no quoting |
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The EVENT → re-enter-wide → decay pattern replaces v1's "stop-loss then sleep
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4 hours via JSON file". Cooldowns are seconds-to-minutes and price-aware, not
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hours and blind.
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## 6. Inventory, exits, and lifecycle risk — without taker orders
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Maker-only exit means **we cannot rely on being able to get out**. The
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controls compensate:
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- **Soft cap `q_soft`**: skew (§3.1) already leans quotes; beyond q_soft the
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adding side is pulled entirely, exit side gains urgency: exit price decays
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from FV + δ toward one tick above best bid over τ_urgency (still maker,
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never crossing). Urgency accelerates if FV drifts against the position.
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- **Hard cap `q_max`**: reduce-only. Sizing (§3.4) makes approaching q_max
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progressively harder, so the hard stop is rarely the active constraint.
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- **No cost-basis anchoring**: v1 refused to ask below avgPrice, converting
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losses into resolution lotteries. v2 prices exits off FV and urgency only;
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cost basis is reporting, not strategy.
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- **Merge always-on**: min(YES, NO) ≥ threshold → merge to USDC (frees
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capital, realizes locked edge, zero market impact).
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- **End-date ladder** (from catalog metadata): T−D days → size taper begins;
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T−H hours → REDUCE_ONLY; T−S → HALTED + cancel. Political markets also
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resolve early: the market WS now pushes **`market_resolved` / `new_market`
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events** (03 §5) — instant halt on resolution — with catalog refresh
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(closed/`acceptingOrders` flags) and the EVENT regime as backstops.
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- **Per-market and global risk** (RiskManager, 02): market notional cap,
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neg-risk **event-group worst-case-loss cap** (multiple candidates in one
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event are one bet, not N), total exposure cap, daily realized-loss kill
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switch (halts quoting, keeps cancel/exit capability), stale-data halt.
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## 7. Neg-risk structural opportunities (optional module, off by default)
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Political events are neg-risk groups; the structure occasionally gifts money:
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- **Set arbitrage**: if Σ best-asks of all outcomes < 1 − ε, buy the set
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(maker where possible, but this is the one place taker math is ever
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justified — behind a config flag, default off, consistent with the
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maker-only policy).
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- **NegRiskAdapter conversions**: NO(i) sets convert to YES(others) + USDC —
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enables inventory rebalancing across outcomes without trading. Scoped in 03
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(§contracts); implementation phase 4+.
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- Both are opportunistic add-ons; core PnL is quoting + rewards. Scoped now so
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the state model (event-group awareness) is built in from day one.
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## 8. Parameters
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`config/strategy.toml` holds named profiles (replaces the Hyperparameters
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sheet); `markets.toml` maps markets → profile + overrides (05 has schemas).
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Illustrative:
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```toml
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[profiles.political-longdated]
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gamma = 0.5 # inventory risk aversion (skew strength)
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delta_min_ticks = 2
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c_vol = 1.2 # spread per unit short-vol
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c_tox = 2.0 # spread per unit toxicity score
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q_max_usdc = 500 # hard inventory cap, notional
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q_soft_frac = 0.6
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layers = 2
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reprice_ticks = 2
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debounce_ms = 200
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event_cooloff_s = 60
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end_date_taper_d = 7
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reduce_only_h = 24
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```
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Every v1 magic number (0.005 reprice, 250 cap, 0.95/0.97/1.01, 15s, 0.1–0.9
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band, `max_size*2`) either becomes a named documented parameter or is deleted
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by design. Hot-reload via watchfiles; changes apply on next quoter wake, no
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restart.
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## 9. Simulation, paper trading, and measurement
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The strategy is only "much better" if we can prove it. Three rungs:
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1. **Replay backtest** (`polymaker sim replay`): the journal (02) captures raw
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WS streams; the sim reconstructs books and runs the strategy with a queue-
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position fill model — conservative mode fills our order only when the tape
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prints *through* our price; optimistic mode fills pro-rata at the touch.
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Truth lies between; both are reported. Reward income is computed from the
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scoring formula on our simulated quotes. Start capturing journals from the
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moment Phase 2 lands, so weeks of political-market data exist before the
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strategy ships.
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2. **Paper mode** (`polymaker run --paper`): full live pipeline, orders
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journaled but not posted; fills simulated from the live tape. The gate for
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live capital.
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3. **Live KPIs** (05 §status): per-market markout curves (+30s/+5m), fill
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ratio, two-sided in-band uptime (the reward KPI), reward $ vs estimate,
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realized/unrealized PnL, inventory duration, EVENT-regime frequency.
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Acceptance criteria to go live (Phase 4 gate, see 06): paper-mode ≥ 2 weeks on
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≥ 10 political markets with (a) positive net PnL including simulated rewards
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under the **conservative** fill model, (b) average +5m markout > −0.5 tick,
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(c) zero risk-limit breaches.
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## 10. Explicit non-goals in v2 (deferred to 07)
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- External data: polls, news, social monitors, other venues (Kalshi et al).
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- Taker execution of any kind (except the flagged neg-risk set-arb, default off).
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- Cross-event statistical arbitrage.
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- Sports/crypto-specific logic — the engine is generic, but tuning and the
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selector target political markets first.
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