Transparent & Data-Driven

Signal Performance & Methodology

We believe in radical transparency. Every confidence score is backed by real backtested data. See exactly how our signals perform across different sources, confidence levels, and market conditions.

Alpha Opportunities Snapshot-Rank Backtest

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Hypothetical $10,000 model on daily alpha_opportunities_snapshot ranks (confidence ≥ 55, same floor as mobile). Top 10 and Top 20 long/short sleeves use non-overlapping 3-trading-day holds: enter at the snapshot day's open when captured pre-market; otherwise next trading day open, exit at close.

  • Point-in-time snapshot rank per direction (Top 10 / Top 20)
  • Equal-weight long and short legs; no overlap while a hold is open
  • No commissions, borrow, slippage, or liquidity constraints
Source
Daily snapshot rows ranked within each direction. Excludes optional web-only filters (60% source win rate, market-cap tiers).
Method
Snapshot-day open when captured pre-market; otherwise next trading day open → close after 3 trading days. Illustration only; not investment advice.

Past simulated performance does not guarantee future results. Large single-window moves may reflect thin or corporate-action price prints.

49.8%
Raw Win Rate
0.03%
Raw Avg Return
319,884
Backtested Signals
5d
Evaluation Horizon

Raw win rate, average return, and backtested signal count above include only resolved signals for the top 500 symbols by market capitalization in our universe (symbols with a known market cap in our database)—not every name we have ever signaled.

Alpha Performance (vs SPY)

The naked truth: performance after removing market movement
-0.06%
Avg Alpha (Excess Return)
49.5%
Beat SPY Rate
0.16%
SPY Avg Return (Same Period)
What is Alpha?
Alpha measures how much our signals beat (or lag) the market. A +1% alpha means signals returned 1% more than SPY over the same period. This strips out market beta—in a +5% SPY rally, a signal returning +6% has +1% alpha. This is the true measure of signal value.
Backtested Performance
Every signal type is tracked against actual market outcomes to calculate real win rates and returns.
Adaptive Learning
Confidence scores adjust automatically as new market data comes in. Poor performers get downweighted.
Full Transparency
No black boxes. See sample sizes, win rates, and methodology for every signal source and type.

Signal Source Reliability

Performance rankings by data source
Source
Raw Win Rate
Raw Return
Alpha
Beat SPY
Samples

Top Performing Signal Types

Win rate > 60% with at least 20 samples
Signal Type
Direction
Win Rate
Weight
Trend
Samples
Form 8-K: Non-Reliance on Financials
Form 8-K Material Events
Bearish
81.8%
High
22
FRED: Producer Price Index
Macro Events
Bearish
75.0%
High
56
SMA Extreme
Statistical Extremes
Bearish
73.3%
High
9,764
Form 8-K: CEO Departure
Form 8-K Material Events
Bearish
71.7%
High
106
RSI Extreme
Statistical Extremes
Bearish
70.1%
High
4,547
Form 8-K: Auditor Change
Form 8-K Material Events
Bearish
70.1%
High
67
Options Flow: IV Skew Bearish
Options Flow
Bearish
66.7%
High
39
Dark Pool: Distribution
Dark Pool Activity
Bearish
66.1%
High
11,430
Volatility Extreme
Statistical Extremes
Bearish
65.3%
High
10,524
Deep Analysis: Valuation
AI Deep Analysis
Bearish
65.3%
High
98

How We Calculate Confidence (0-100 Scale)

Our 3-step formula for signal scoring
1

Base Confidence

We calculate a weighted average of all agreeing signals:

Base = Σ(Win Rate × Weight) / Σ(Weight)

Example: Congress (55% win rate, 0.70 weight) + Dark Pool (65%, 0.78) = 61 base

2

Confluence Boost

Multiple independent signals get a bonus:

  • 2 signals: +3 points
  • 3 signals: +7 points
  • 4+ signals: +10 points
3

Diversity Bonus (0-8 points)

Diverse signal types and sources get additional boost:

  • Type diversity: +0 to +5 points
  • Source diversity: +0 to +3 points

Example: 4 different signal types from 3 sources = +8 diversity bonus

=

Final Score

Base + Confluence + Diversity = Final Confidence (0-100)

Example: 61 + 7 + 8 = 76 (High Confidence)

Confidence Tiers

75-100: High Confidence
65-74: Medium Confidence
55-64: Low Confidence
Below 55: Not displayed publicly

Our Signals Learn From Performance

Adaptive weighting based on recent performance

Unlike static systems, our signal weights adapt based on recent performance:

Example: Dark Pool Signals

All-Time: 65% win rate → 0.78 weight
Last 30 Days: 72% win rate → 0.82 weight ↑

Recent strong performance increases the signal's influence on future confidence scores.

Weight Calculation

30-Day Win Rate Weight
≥70%0.90 (Maximum influence)
65-69%0.80 (High influence)
60-64%0.70 (Good influence)
55-59%0.60 (Moderate influence)
50-54%0.50 (Low influence)
<50%0.40 (Minimal influence)