kalshi backtest progress
===

this document tracks the development progress, algorithm details, and backtest results for the kalshi prediction market trading system.

last updated: 2026-01-22


backtest run #1
---

**date:** 2026-01-22
**period:** 2026-01-20 to 2026-01-22 (2 days)
**initial capital:** $10,000
**interval:** 1 hour

### results summary

| metric | strategy | random baseline | delta |
|--------|----------|-----------------|-------|
| total return | +$993.61 (+9.94%) | -$51.00 (-0.51%) | +$1,044.61 |
| sharpe ratio | 5.448 | -2.436 | +7.884 |
| max drawdown | 1.26% | 0.51% | +0.75% |
| win rate | 58.7% | 0.0% | +58.7% |
| total trades | 46 | 0 | +46 |
| avg trade pnl | $4.59 | $0.00 | +$4.59 |
| avg hold time | 5.4 hrs | 0.0 hrs | +5.4 hrs |

### notable trades

| ticker | entry | exit | side | pnl | hold time |
|--------|-------|------|------|-----|-----------|
| KXKHLGAME-26JAN21SEVHCS-SEV | $0.17 | $0.99 | Yes | +$81.98 | 1h |
| KXKHLGAME-26JAN21SEVHCS-HCS | $0.20 | $0.93 | Yes | +$72.98 | 1h |
| KXFIRSTSUPERBOWLSONG-26FEB09-DTM | $0.11 | $0.63 | Yes | +$51.99 | 2h |
| KXUCLBTTS-26JAN21OMLFC | $0.01 | $0.50 | Yes | +$49.00 | 1h |
| KXNCAAWBGAME-26JAN21BULAF-LAF | $0.43 | $0.80 | No | +$36.96 | 3h |

### worst trades

| ticker | entry | exit | side | pnl | hold time |
|--------|-------|------|------|-----|-----------|
| KXUCLBTTS-26JAN21GALATM | $0.40 | $0.01 | No | -$39.04 | 1h |
| KXUCLBTTS-26JAN21QARSGE | $0.35 | $0.01 | No | -$34.04 | 7h |
| KXFIRSTSUPERBOWLSONG-26FEB09-CAL | $0.35 | $0.07 | Yes | -$28.03 | 12h |
| KXUCLGAME-26JAN21ATAATH-ATA | $0.46 | $0.19 | No | -$27.05 | 14h |


algorithm architecture
===

pipeline overview
---

the system uses a modular pipeline architecture with four stages:

```
sources -> filters -> scorers -> selector
```

1. **sources** - retrieve market candidates from historical data
2. **filters** - remove unsuitable markets
3. **scorers** - compute feature scores for each candidate
4. **selector** - pick top-k candidates for trading

current configuration
---

### sources

| source | config |
|--------|--------|
| HistoricalMarketSource | lookback: 24 hours |

### filters

| filter | config | purpose |
|--------|--------|---------|
| LiquidityFilter | min_volume_24h: 100 | reject illiquid markets |
| TimeToCloseFilter | min: 2h, max: 720h | avoid expiring/distant markets |
| AlreadyPositionedFilter | max_position: 100 | prevent over-concentration |

typical filter stats (per interval):
- ~17,000 candidates retrieved
- ~10,000 pass liquidity filter (~58%)
- ~7,200 pass time filter (~72% of remaining)
- ~7,150 pass position filter (~99% of remaining)

### scorers

the pipeline runs 8 independent scorers that each contribute features:

| scorer | features | lookback | description |
|--------|----------|----------|-------------|
| MomentumScorer | `momentum` | 6h | price change over lookback window |
| MultiTimeframeMomentumScorer | `mtf_momentum`, `mtf_divergence`, `mtf_alignment` | 1h, 4h, 12h, 24h | multi-window momentum with divergence detection |
| MeanReversionScorer | `mean_reversion` | 24h | deviation from historical mean |
| BollingerMeanReversionScorer | `bollinger_reversion`, `bollinger_position` | 24h, 2.0 std | statistical band analysis |
| VolumeScorer | `volume` | 6h | log ratio of recent vs avg hourly volume |
| OrderFlowScorer | `order_flow` | - | buy/sell imbalance from taker_side |
| TimeDecayScorer | `time_decay` | - | time value decay factor |
| CategoryWeightedScorer | `final_score` | - | category-specific weighted ensemble |

### category-specific weights

the CategoryWeightedScorer applies different weight profiles based on market category:

**default weights:**
```
momentum:        0.20
mean_reversion:  0.20
volume:          0.15
time_decay:      0.10
order_flow:      0.15
bollinger:       0.10
mtf_momentum:    0.10
```

**politics:**
```
momentum:        0.35  (trend-following works)
mean_reversion:  0.10
order_flow:      0.15
mtf_momentum:    0.15
```

**weather:**
```
mean_reversion:  0.35  (converges to forecasts)
bollinger:       0.15
time_decay:      0.15
```

**sports:**
```
order_flow:      0.30  (sharp money matters)
momentum:        0.20
volume:          0.15
```

**economics/financial:**
```
momentum:        0.25
mean_reversion:  0.20
volume:          0.15
```

### selector

| selector | config |
|----------|--------|
| TopKSelector | k=5 (max_positions) |


execution logic
===

position sizing
---

uses fractional kelly criterion for position sizing:

```rust
kelly_fraction = 0.25          // use 25% of kelly optimal
max_position_pct = 0.25        // max 25% of portfolio per trade
min_position_size = 10         // minimum 10 contracts
max_position_size = 100        // maximum 100 contracts
```

**edge to probability mapping:**
```
win_prob = (1 + tanh(edge)) / 2
```

this smoothly maps scoring edge to estimated win probability.

**kelly formula:**
```
kelly = (odds * win_prob - (1 - win_prob)) / odds
position_value = bankroll * min(kelly * kelly_fraction, max_position_pct)
```

side selection
---

the executor picks the cheaper side based on signal direction:

- positive score (bullish) + yes_price < 0.5 -> buy YES
- positive score (bullish) + yes_price >= 0.5 -> buy NO
- negative score (bearish) + yes_price > 0.5 -> buy NO
- negative score (bearish) + yes_price <= 0.5 -> buy YES

rationale: buying the cheaper side gives better risk/reward ratio.

exit conditions
---

positions are closed when any of these trigger:

| condition | threshold | description |
|-----------|-----------|-------------|
| take_profit | +20% | lock in gains |
| stop_loss | -15% | limit downside |
| time_stop | 72 hours | prevent stale positions |
| score_reversal | < -0.3 | signal flipped against us |

slippage model
---

- 10 bps slippage applied to all fills
- limit orders rejected if fill price exceeds limit by 2x slippage


data characteristics
===

current dataset: `/mnt/work/kalshi-data/`

| file | size | description |
|------|------|-------------|
| markets.csv | 6.6 GB | market metadata, results, prices |
| trades.csv | 66 MB | individual trade records with taker_side |

trade record schema:
```
timestamp, ticker, price, volume, taker_side
```

market record schema:
```
ticker, title, category, open_time, close_time, result, status,
yes_bid, yes_ask, volume, open_interest
```


known issues / future work
===

### issues

1. **empty categories** - return_by_category shows empty string, need to verify category parsing from market data

2. **no trading on jan 20** - equity curve shows no activity until jan 21 04:00, likely due to insufficient trade history in lookback window

3. **dead code warnings** - several unused scorers and filters (CorrelationScorer, MLEnsembleScorer, etc.) - cleanup needed

### planned improvements

- [ ] category parsing fix
- [ ] correlation scorer integration (granger causality between related markets)
- [ ] ML model integration (ONNX runtime ready, needs trained models)
- [ ] multi-day backtests with larger date ranges
- [ ] parameter optimization / grid search
- [ ] transaction cost analysis
- [ ] position-level attribution


appendix: scorer formulas
===

### momentum

```
momentum = price(t) - price(t - lookback_hours)
```

### mean reversion

```
mean = avg(prices over lookback_hours)
deviation = current_price - mean
mean_reversion = -deviation
```

### bollinger bands

```
mean = avg(prices)
std = stddev(prices)
upper_band = mean + 2.0 * std
lower_band = mean - 2.0 * std

if price >= upper_band:
    score = -(price - upper_band) / std
elif price <= lower_band:
    score = (lower_band - price) / std
else:
    score = -0.5 * (position - 0.5)  // weak mean reversion inside bands
```

### volume

```
avg_hourly_volume = total_volume / hours_since_open
recent_hourly_volume = recent_volume / lookback_hours
volume_score = ln(recent_hourly_volume / avg_hourly_volume)
```

### order flow

```
order_flow = (buy_volume - sell_volume) / (buy_volume + sell_volume)
```

### time decay

```
hours_remaining = time_to_close
time_decay = 1 - 1 / (hours_remaining / 24 + 1)
```

ranges from 0 (about to close) to ~1 (distant expiry).