Quantitative Analytics Suite
Every metric institutional desks use. P&L attribution, alpha decomposition, Kelly sizing, risk matrices, Monte Carlo forward simulation, and execution quality -- all computed in real time.
Know Where Every Dollar Came From
Decompose your returns by strategy, market category, time period, and individual agent. Instantly see which strategies carry the portfolio and which drag it down. Attribution updates tick-by-tick as positions resolve.
- Strategy-level P&L with daily, weekly, monthly rollups
- Market category breakdown (politics, sports, crypto, macro)
- Per-agent contribution with win rate and edge metrics
{
"total_pnl": +$4,281.40,
"by_strategy": {
"momentum": +$2,140.20 // 50.0%
"sentiment": +$1,830.50 // 42.8%
"contrarian": +$ 620.70 // 14.5%
"mean_revert": -$ 310.00 // -7.2%
},
"by_market": {
"politics": +$2,450.00,
"crypto": +$1,120.30,
"sports": +$ 890.10,
"weather": -$ 179.00
}
}
BENCHMARK COMPARISON
─────────────────────────────────────
Portfolio Return: +18.4%
Kalshi Avg Return: +6.2%
Alpha (excess): +12.2%
Information Ratio: 2.14
SIGNAL SCORING (top 5)
─────────────────────────────────────
1. news_catalyst Score: 0.84 Decay: 4.2h
2. momentum_12h Score: 0.76 Decay: 8.1h
3. poll_divergence Score: 0.71 Decay: 18h
4. whale_tracking Score: 0.68 Decay: 2.1h
5. twitter_sentiment Score: 0.42 Decay: 0.8h
DECAY DETECTION
─────────────────────────────────────
WARNING: twitter_sentiment alpha
decayed 61% over past 14d.
Recommendation: reduce weight or retire.
Measure the Edge That Matters
Compare returns against Kalshi market averages, random walk baselines, and custom benchmarks. Signal decay detection monitors each alpha source over time, alerting you when a previously profitable signal is losing predictive power.
- Benchmark comparison: market avg, random walk, custom
- Signal scoring with predictive power ranking
- Automatic decay detection with retirement alerts
Position Sizes You Can Audit
Full Kelly, fractional Kelly, and drawdown-adjusted Kelly -- computed in real time for each eligible trade. Ruin probability monitoring and drawdown adjustments help keep sizing decisions inside your configured risk limits.
- Full Kelly for maximum geometric growth rate
- Fractional Kelly (quarter, half, three-quarter)
- Drawdown-adjusted sizing with ruin probability guard
{
"market": "Will Fed cut rates in Q2?",
"estimated_edge": 0.142,
"market_price": 0.58,
"model_prob": 0.722,
"kelly_full": {
"fraction": 0.246,
"position": $246.00,
"growth_rate": 1.74%
},
"kelly_half": {
"fraction": 0.123,
"position": $123.00,
"growth_rate": 1.31%
},
"drawdown_adjusted": {
"current_dd": -8.4%,
"dd_multiplier": 0.72,
"adj_position": $89.00
},
"ruin_probability": {
"at_full_kelly": 4.2%,
"at_half_kelly": 0.3%,
"at_dd_adjusted": 0.1%
}
}
| Metric | Value | Limit | Status |
|---|---|---|---|
| Daily VaR (95%) | -$312 | -$500 | OK |
| Daily CVaR (99%) | -$487 | -$750 | OK |
| Max Drawdown | -8.4% | -15% | OK |
| Current Drawdown | -3.1% | -15% | OK |
| Correlation Max | 0.82 | 0.70 | WARN |
| Regime | TRENDING (0.74 conf) | -- | |
POL 1.00 0.12 0.31 0.82
SPT 0.12 1.00 -0.08 0.15
CRY 0.31 -0.08 1.00 0.44
MAC 0.82 0.15 0.44 1.00
ALERT: POL-MAC correlation 0.82 exceeds
0.70 threshold. Consider reducing overlap.
Institutional-Grade Risk Management
Correlation matrices detect hidden concentration risk. Regime detection identifies trending, mean-reverting, or crisis markets and adjusts risk parameters automatically. VaR and CVaR at multiple confidence levels with drawdown circuit breakers.
- Correlation matrices with auto concentration alerts
- Regime detection: trending, mean-revert, crisis
- VaR/CVaR at 95% and 99% with real-time updates
- Drawdown circuit breakers with automatic de-risking
Replay the Past, Simulate the Future
Historical backtesting replays your strategy against every resolved market -- no lookahead bias, no survivorship bias. Monte Carlo runs 10,000 forward paths with confidence intervals on returns, drawdowns, and probability of hitting your target.
- Historical replay on all resolved markets
- 10,000 forward Monte Carlo simulations
- Confidence intervals at 50th, 75th, 95th, 99th percentiles
--start 2024-01-01 --monte-carlo 10000
HISTORICAL BACKTEST (24 months)
─────────────────────────────────────
Total Trades: 1,847 Win Rate: 58.3%
Avg Return: +2.31% Sharpe: 1.89
Max Drawdown: -14.2% Recovery: 18d
MONTE CARLO FORWARD (10K paths, 90d)
─────────────────────────────────────
Percentile Return MaxDD
───────────────────────────────
1st -22.4% -31.0%
5th -8.1% -18.7%
25th +4.2% -9.4%
50th +12.8% -6.1%
75th +21.6% -4.2%
95th +38.4% -2.8%
99th +52.1% -1.9%
Prob(profit in 90d): 71.4%
Prob(ruin): 0.4%
| Metric | This Month | Avg | Impact |
|---|---|---|---|
| Total Trades | 342 | -- | -- |
| Avg Slippage | -0.8c | -1.2c | -$27.36 |
| Platform Fees | $171.00 | $158.40 | -3.99% |
| Fill Rate | 97.4% | 95.8% | IMPROVED |
| Net Edge After Costs | +8.2% | +7.6% | IMPROVED |
per-trade fee ratio. Est. savings: $24/mo
2. Shift 18% of volume to limit orders.
Est. slippage reduction: -0.4c avg
3. Avoid first 30s after market open --
slippage is 3.2x higher. Saves: $18/mo
Every Cent Leaking From Your Edge
Track execution quality across every trade. Measure slippage between signal price and fill price, compute fee drag as a percentage of gross P&L, and get concrete optimization suggestions with estimated dollar savings.
- Per-trade slippage measurement (signal vs. fill)
- Fee drag computation with net-of-cost edge
- Automated optimization suggestions with $ estimates
Compare Everything Against Everything
Period-over-period analysis, strategy A/B testing with statistical significance, and regime-conditional comparisons. See how strategies behave differently in trending vs. mean-reverting vs. crisis environments.
- Period vs. period with delta highlighting
- Strategy A/B testing with significance tests
- Regime-conditional performance breakdowns
| Metric | Feb 2026 | Mar 2026 | Delta |
|---|---|---|---|
| Net P&L | +$1,840 | +$2,441 | +32.7% |
| Win Rate | 55.1% | 59.8% | +4.7pp |
| Avg Edge | 8.4c | 11.2c | +2.8c |
| Max DD | -12.1% | -8.4% | +3.7pp |
| Sharpe | 1.42 | 1.89 | +0.47 |
{
"variant_a": "momentum_v2",
"variant_b": "momentum_v3",
"return_a": +9.8%,
"return_b": +14.2%,
"p_value": 0.023,
"significant": true,
"regime_breakdown": {
"trending": v3 +6.1% vs v2 +3.8%,
"mean_revert": v3 +1.2% vs v2 +2.4%,
"crisis": v3 -2.1% vs v2 -5.8%
}
}
Beyond basic backtesting
Institutional-grade analysis tools that separate signal from noise.
Walk-Forward Analysis
5 rolling windows, 70/30 train-test split. Measures degradation between in-sample and out-of-sample performance to detect overfitting before it costs you money.
Correlation Matrix
Strategy-to-strategy correlation heatmap. Auto-alerts when hidden concentration risk exceeds thresholds. Diversification scoring for your agent fleet.
6-Factor Attribution
Decompose returns by time-of-day, spread, volume, price level, momentum, and liquidity. Know exactly which factors drive your alpha and which are noise.
4-Regime Analysis
High-vol, normal, low-vol, and trending regimes. Per-regime win rate, PnL, and trade count so you know when your strategy thrives and when to sit out.
Parameter Optimization
Automated parameter sweep with suggested ranges for lookback periods, thresholds, and position sizing. Impact scoring tells you which params matter most.
Fee Impact Breakdown
Gross vs. net PnL waterfall. Exchange fees, spread costs, and settlement fees separated. See your fee-adjusted Sharpe ratio and break-even trade count.
Seven Pillars of Quantitative Analysis
Everything you need to run a data-driven prediction market portfolio.
Stop Guessing. Start Measuring.
Portfolio analytics, risk metrics, execution quality, and agent comparisons without needing to maintain a separate quant stack.