# Hermes v4.0 - Research Findings & Pending Fixes

**Source:** `kalshi-weather-research.pdf` (compiled March 25, 2026)

**Date:** 2026-03-26 (updated 2026-04-03)

**Status:** Fixes 1-2-7 implemented. Bracket fix patch deployed 2026-04-01. Net EV filter deployed 2026-04-03.

---

## 2026-04-03 - Net EV Dust Trade Filter (Deployed)

**Root cause found:** Kelly sizing with dampeners can produce bets that buy only 1 contract

on high-priced markets (e.g. KXHIGHLAX-26APR03-T74: 1 contract @ $0.64 = $0.31 net EV).

These dust trades waste one of 8 daily trade slots for pennies of expected value.

**Fix deployed (CT-REDACTED + CT-REDACTED):**

| Fix | What | Why |

|-----|------|-----|

| `MIN_TRADE_EV_PCT = 0.0015` | New guardrail constant (0.15% of bankroll) | Scales threshold with account size |

| Net EV gate | `net_ev = contracts × (bet_prob - ask_price) - fees` | Filters on expected dollars, not arbitrary contract counts |

| Skip + log | Skipped trades logged with EV, threshold, contract count | Full audit trail in scan actions / Discord |

**Why net EV over simpler alternatives:**

- Min contracts floor (e.g. 3): crude, doesn't account for price differences

- Raised min bet ($2): ignores whether the trade actually generates value

- Net EV: directly measures expected dollars per trade slot, captures fee drag, scales with bankroll

**Thresholds at current balances:**

- Hermes ($348): min EV = $0.52/trade

- Hermes2 ($49): min EV = $0.07/trade

---

## 2026-04-01 - Bracket Fix Patch (Deployed)

**Root cause found:** `_ensemble_gaussian_bracket()` systematically underestimates bracket

probability when the ensemble has converged (sigma < 2°F). Outliers inflate Gaussian sigma,

spreading probability outside the bracket. Example: Chicago high ensemble converged to

40-41°F range, Gaussian said 31% bracket probability, raw count showed 74%. This inflated

NO edge above the 0.20 veto ceiling, causing risky bracket trades to bypass Sonnet review.

**4 fixes deployed (CT-REDACTED + CT-REDACTED):**

| Fix | What | Why |

|-----|------|-----|

| Hybrid bracket prob | `max(Gaussian, raw_count±0.5°F)` | Gaussian helps far-out; raw catches converged |

| Bracket veto trigger | Ensemble mean inside bracket → Sonnet review | Safety net for riskiest bracket scenario |

| Bet sizing hard cap | `min(bet, 8% × bankroll)` after Kelly | Prevents rounding overshoot |

| Raw prob column | `raw_ensemble_probability` in trades table | Audit: separate raw vs calibrated |

**Rejected after backtesting:**

- ±2°F NWS guard: Blocked 7 winners, 0 losses = -$16.19 net. NWS distance is NOT a predictor of bracket failure.

- METAR entry filter: Dead code. Trades placed 12-30h before observations become informative.

**Planned (weekend):** Bracket exit monitor - sells positions when 6 gates confirm edge flip.

**Key data points:**

- Historical bracket NO: 15W/6L, +$5.56 net, 71% win rate

- Losses cluster at NWS 3-10°F from bracket (big forecast busts), NOT near-bracket trades

- The live code already had a 50/50 blend + 5% floor approach - replaced with max() which is more accurate

---

## Fixes To Implement (Priority Order)

### FIX 1: Variable Fee Formula (CRITICAL - blocking profitable trades now)

**Current:** Flat `TAKER_FEE_PER_CONTRACT = 0.05` applied to all trades.

**Correct:** `fee = ceil(0.07 * contracts * price * (1 - price))`

| Contract Price | Our Fee (flat) | Actual Fee | We're Wrong By |

|---------------|---------------|------------|----------------|

| $0.85 | $0.05 | $0.01 | 5x too high |

| $0.75 | $0.05 | $0.02 | 2.5x too high |

| $0.60 | $0.05 | $0.02 | 2.5x too high |

| $0.50 | $0.05 | $0.02 | 2.5x too high |

| $0.40 | $0.05 | $0.02 | 2.5x too high |

**Impact:** We're rejecting trades with 7-9% true edge because our inflated fee estimate makes them look below the 10% threshold. This is the single biggest leak in the system right now.

**Implementation:**

```python

def kalshi_taker_fee(price, contracts=1):

    import math

    raw_fee = 0.07 * contracts * price * (1 - price)

    return math.ceil(raw_fee * 100) / 100

```

Replace the flat constant everywhere edge is calculated.

---

### FIX 2: Reduce Scan Interval from 30 Minutes to 5 Minutes

**Current:** Scanner runs every 30 minutes.

**Finding:** The competing bot (suislanchez, $1,325+ profit) scans every 5 minutes.

**Cost:** Zero. 1,440 API calls/day vs 10,000 limit.

**Benefit:** Catches mispricing faster, especially after GFS ensemble releases (data available ~3.5h after initialization at 00Z/06Z/12Z/18Z).

**Risk:** More Sonnet veto calls on Max plan. Mitigated by the filter pipeline - most markets get rejected before reaching Sonnet.

---

### FIX 3: Add Maker Orders for Better Fees

**Current:** All orders are taker (market) orders.

**Finding:** Maker fee is 25% of taker fee: `ceil(0.0175 * C * P * (1-P))`. At $0.50 contract: taker fee = $0.02, maker fee = $0.01.

**Implementation:** For trades where market is not about to close (>6h to settlement), place limit orders slightly inside the spread instead of taking the ask. Fall back to taker if not filled within 10 minutes.

**Complexity:** Medium - requires order monitoring and cancellation logic.

**Priority:** After fix 1 and 2 are validated.

---

### FIX 4: Extremized Aggregation (Replace Simple Calibration Multiply)

**Current:** `adj_prob = ens_prob * calibration_multiplier`

**Better:** Combine ensemble + NWS + base rates via log-odds with extremizing factor.

**Research:** Satopaa et al. (2014), Neyman & Roughgarden (2021) - optimal factor ~1.73 for robust aggregation.

**Implementation:**

```python

def extremize_aggregate(probabilities, weights=None, factor=1.5):

    import math

    if weights is None:

        weights = [1.0 / len(probabilities)] * len(probabilities)

    clamped = [max(0.001, min(0.999, p)) for p in probabilities]

    log_odds = [math.log(p / (1 - p)) for p in clamped]

    avg_lo = sum(w * lo for w, lo in zip(weights, log_odds))

    ext_lo = avg_lo * factor

    return 1 / (1 + math.exp(-ext_lo))

```

**Notes:**

- Start with factor 1.5 (conservative - ensemble members share model physics, high info overlap)

- Weights: 0.5 ensemble, 0.3 NWS, 0.2 historical base rate

- Factor for weather should be lower than geopolitical (1.73) because ensemble members aren't independent

**Priority:** After 30 trades validate the current system works.

---

### FIX 5: Rain Ensemble Bias Correction

**Finding:** GFS ensemble over-forecasts light precipitation (false alarm rate too high). Raw member counting overestimates "any rain" probability.

**Source:** Zhu & Luo (2015), "Precipitation Calibration Based on the Frequency-Matching Method"

**Implementation:** Maintain rolling 30-day comparison of ensemble rain probability vs observed rain for each city. Apply frequency-matching correction.

**Priority:** After accumulating 20+ KXRAIN settlements to establish baseline bias.

---

### FIX 6: City-Specific Low Temp Adjustments

**Finding:** Overnight lows are harder to forecast than highs due to radiative cooling, inversions, UHI effects.

**Risk ranking:**

| City | Low Temp Risk | Reason |

|------|-------------|--------|

| Denver | HIGHEST | Altitude + inversions + dry air + DEN airport 24mi from downtown |

| Chicago | HIGH | Lake effect + continental + inversion potential |

| NYC | MEDIUM | UHI 8F+, airport vs Manhattan can differ 5F+ at night |

| LA | MEDIUM | LAX coastal vs inland can differ 10-15F on summer nights |

| Miami | LOWEST | Tropical maritime limits radiative cooling |

**Implementation:** Apply higher minimum edge for KXLOWT than KXHIGH. Possible: 12% for low vs 10% for high, with Denver KXLOWT at 15%.

**Priority:** Can implement now as a constant, tune after data.

---

### FIX 7: Reduce Edge Threshold from 10% to 8%

**Finding:** The suislanchez bot uses 8% edge threshold and is profitable. With the variable fee fix (Fix 1), our true edge calculation becomes more accurate, so a lower threshold is justified.

**Current:** `MIN_EDGE = 0.10`

**Proposed:** `MIN_EDGE = 0.08` (matches competitor)

**Caveat:** Only after Fix 1 (variable fees) is implemented. With the flat $0.05 fee, 8% would let in bad trades.

**Priority:** Implement together with Fix 1.

---

### FIX 8: GFS Ensemble Release-Aware Scanning

**Finding:** GFS ensemble data becomes available ~3.5h after initialization:

| Run | Init (UTC) | Data Available | CDT |

|-----|-----------|----------------|-----|

| 00Z | 00:00 | ~03:30 UTC | 10:30 PM |

| 06Z | 06:00 | ~09:30 UTC | 4:30 AM |

| 12Z | 12:00 | ~15:30 UTC | 10:30 AM |

| 18Z | 18:00 | ~21:30 UTC | 4:30 PM |

**Implementation:** After switching to 5-minute scans, no special timing needed - the bot naturally picks up new data. But could log which GFS run the ensemble came from for calibration purposes.

**Priority:** Low - 5-minute scans handle this implicitly.

---

## Research Findings (Reference - No Code Changes Needed)

### FINDING 1: Kalshi Balance Earns 3.75-4% APY

Kalshi pays yield on total account balance. At $60 this is negligible ($2.25/year), but at $500+ it becomes a consideration - idle cash isn't fully idle.

### FINDING 2: Maker Fee History - Rounding Exploit Was Real

Before July 2025, maker fees were flat $0.0025/contract. On $0.02 contracts, this rounded to $0.01 - a 50% effective fee. Kalshi fixed this. Current variable formula eliminates the rounding issue.

### FINDING 3: Post-2024 Kalshi Regime Change

Before 2024 Q4, takers made money on average. After Kalshi's legal victory and volume explosion ($30M to $820M/quarter), professional market makers entered. Takers now lose on average. Our edge MUST come from better information (ensemble data), not from market structure.

### FINDING 4: Weather is in the "Other" Category at ~10% of Volume

Weather/climate is Kalshi's original niche but only ~10% of total notional volume. Lower volume = potentially wider spreads but also less competition from sophisticated market makers.

### FINDING 5: Longshot Bias Confirmed with Kalshi Data

72.1M trade analysis confirms: contracts below 10% implied probability consistently underperform for buyers. Our $0.40 price floor already exploits this by forcing us to trade in the 40-99 cent range where mispricing exists without the longshot trap.

### FINDING 6: Becker Dataset Available for Backtesting

Full Parquet dataset at github.com/Jon-Becker/prediction-market-analysis. Could filter to weather tickers and compute actual historical mispricing, time-of-day effects, and pre/post ensemble release patterns.

### FINDING 7: NBM May Be Superior to Raw GFS Ensemble

The National Blend of Models (NBM v4.3) already applies bias correction + quantile mapping to GFS/GEFS/HRRR/ECMWF. Could be used as a third probability source alongside ensemble and NWS for extremized aggregation.

### FINDING 8: Fan & van den Dool (2011) Key Results

- GFS ensemble 2m temp errors are dominated by large-scale spatial patterns

- 30-day mean forecast errors produce more robust bias corrections than 7-day means

- Cold season shows more removable bias than warm season

- ~60% of total error variance captured by leading EOF modes

### FINDING 9: UHI Effect Is Larger at Night

Urban Heat Island effect is 2-5F warmer at night (more than daytime 1-7F). Since Kalshi settles on airport METAR stations (often outside UHI), the model grid cell may include urban warming the airport doesn't see. This creates a systematic warm bias in low temp ensemble forecasts for urban stations.

### FINDING 10: GEFS Reforecast Data Available on AWS

`s3://noaa-gefs-retrospective/GEFSv12/reforecast/` - could build city-specific MAE/bias tables from 2000-present instead of waiting for live trade data. 11 ensemble members (vs 31 operational) but large historical sample.

---

## Open-Source Bot Comparison

| Aspect | suislanchez Bot | Hermes v4 |

|--------|----------------|-----------|

| Profit | $1,325+ confirmed | $0 (just deployed) |

| Edge threshold | 8% | 10% (should lower to 8%) |

| Scan interval | 5 minutes | 30 minutes (should lower to 5) |

| Kelly fraction | 15% (0.15x) | Variable (0.125-0.375x by confidence) |

| Markets | KXHIGH only | KXHIGH + KXLOWT + KXRAIN |

| Fee model | Unknown | Variable formula (pending fix) |

| NWS cross-check | No | Yes (high, low, rain) |

| Sonnet veto gate | No | Yes |

| Correlation guard | No | Yes (2 per city/date) |

| Calibration | Brier only | Per-type per-city per-season |

---

## Implementation Order

| Phase | Fixes | When |

|-------|-------|------|

| **Now** | Fix 1 (variable fees) + Fix 7 (lower threshold to 8%) | Immediate |

| **This week** | Fix 2 (5-min scans) + Fix 6 (city-specific low temp adjustments) | After Fix 1 validated |

| **After 30 trades** | Fix 4 (extremized aggregation) + Fix 5 (rain bias correction) | Need data first |

| **After 50 trades** | Fix 3 (maker orders) + Fix 8 (release-aware logging) | Optimization phase |