stonks-oracle/docs/equations.md

Stonks Oracle — Mathematical Reference

Every equation, formula, threshold, and constant used in the signal processing, aggregation, recommendation, and trading pipeline. Organized by pipeline stage.

Code references are provided so each formula can be traced to its implementation.

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1. Signal Scoring

Source: services/aggregation/scoring.py

1.1 Combined Signal Weight

Each document signal receives a composite weight:

W_combined = G_conf × W_recency × W_credibility × (1 + B_novelty) × M_context

Component	Symbol	Formula	Range
Confidence gate	G_conf	1 if extraction_confidence ≥ 0.2, else 0	{0, 1}
Recency decay	W_recency	2^(−t_age / t_half)	[0.01, 1.0]
Credibility	W_credibility	clamp(credibility, 0.1, 1.0)^α	[0.1, 1.0]
Novelty bonus	B_novelty	novelty_score × 0.25	[0, 0.25]
Market context	M_context	1 + boost_vol + boost_vol_surge	[1.0, 1.45]

1.2 Recency Decay

W_recency = max( 2^(−t_age / t_half), 0.01 )

where t_age is document age in hours and half-lives by window are:

Window	t_half (hours)
intraday	2
1d	12
7d	72
30d	240
90d	720

1.3 Credibility Weight

W_credibility = clamp(c_raw, 0.1, 1.0)^α       where α = 1.0 (default)

α > 1 penalizes low-credibility sources more aggressively; α < 1 flattens the curve.

1.4 Market Context Multiplier

boost_vol = min( ln(1 + max(σ − 1.0, 0)) × 0.15, 0.30 )

boost_surge = 0.15   if ΔV% > 50%,  else 0

M_context = 1.0 + boost_vol + boost_surge

where σ is price volatility and ΔV% is volume change percentage.

1.5 Weighted Sentiment Average

S_avg = Σ(W_combined_i × impact_i × sentiment_i) / Σ(W_combined_i × impact_i)

• sentiment_i ∈ {+1.0 (positive), −1.0 (negative), 0.0 (neutral/mixed)}
• impact_i ∈ [0, 1] from extraction
• Returns 0.0 when denominator = 0

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1B. Probabilistic Signal Scoring (Feature-Flagged)

Source: services/aggregation/scoring.py Active when: probabilistic_scoring_enabled = true in risk_configs.config JSONB

When the probabilistic pipeline is enabled, the combined weight formula changes:

1B.1 Combined Signal Weight (Probabilistic)

W_combined = G_sigmoid × W_recency(adaptive) × W_credibility × (1 + B_novelty) × R_info × F_accuracy × M_regime

Component	Symbol	Formula	Range
Sigmoid gate	G_sigmoid	σ(k·(x − midpoint)) = 1/(1+e^(−5·(x−0.5)))	(0, 1)
Adaptive recency	W_recency	2^(−t_age / τ_adaptive)	[0.01, 1.0]
Credibility	W_credibility	same as heuristic	[0.1, 1.0]
Novelty bonus	B_novelty	same as heuristic	[0, 0.25]
Information gain	R_info	1 + λ·(−log₂ P(event_type))	[1.0, 3.0]
Source accuracy	F_accuracy	0.5 + accuracy_ratio (if samples ≥ 10, else 1.0)	[0.5, 1.5]
Regime multiplier	M_regime	1 + 0.15·	z_r

1B.2 Sigmoid Confidence Gate

Replaces the binary 0/1 gate with a smooth transition:

G_sigmoid = σ(k·(x − m)) = 1 / (1 + e^(−k·(x−m)))

Default: k = 5.0, m = 0.5. At x=0.5 → 0.5; at x=0.2 → ~0.18; at x=0.8 → ~0.82.

1B.3 Information Gain (Surprise Weighting)

R_info = min(1 + λ·(−log₂ P(event_type)), 3.0)

Event Type	P(event_type)	R_info (λ=0.3)
earnings	0.25	1.60
dividend	0.15	1.84
product_launch	0.10	2.00
regulatory	0.08	2.07
management_change	0.06	2.19
legal	0.05	2.29
restructuring	0.04	2.39
m_and_a	0.03	2.56
unknown	0.10 (default)	2.00

1B.4 Adaptive Recency Decay

τ_adaptive = τ_base × (1 + β_impact) × (1 + β_surprise) × (1 + β_market)

Factor	Formula	Range
β_impact	impact_score × 1.0	[0, 1.0]
β_surprise	(R_info − 1) / 2 × 1.0	[0, 1.0]
β_market	(M_regime − 1) / 0.45 × 0.5	[0, 0.5]

Maximum adaptive half-life: 6× base (when all factors at max). Minimum: τ_base (adaptive decay is never faster than fixed).

1B.5 Regime Multiplier

z_r = (r_t − μ_20) / σ_20          (return z-score)
z_v = (ln(V_t) − μ_V) / σ_V       (log-volume z-score)
M_regime = clamp(1 + 0.15·|z_r| + 0.10·|z_v|, 1.0, 2.5)

Defaults to 1.0 when market data unavailable or σ = 0.

1B.6 Source Accuracy Factor

F_accuracy = 0.5 + clamp(accuracy_ratio, 0, 1)    if sample_count ≥ 10
F_accuracy = 1.0                                    if sample_count < 10

Stored in source_accuracy table, updated asynchronously from realized 7-day price outcomes.

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2. Trend Summary Assembly

Source: services/aggregation/worker.py

2.1 Trend Direction

Condition	Direction
S_avg ≥ 0.15	Bullish
S_avg ≤ −0.15	Bearish
contradiction > 0.10 AND	S_avg
otherwise	Neutral

2.2 Trend Strength

strength = min(|S_avg|, 1.0)

2.3 Contradiction Score

Source: services/aggregation/contradiction.py

contradiction = W_minority / (W_positive + W_negative)

where:

W_positive = Σ(W_combined_i × impact_i)   for signals with sentiment > 0
W_negative = Σ(W_combined_i × impact_i)   for signals with sentiment < 0
W_minority = min(W_positive, W_negative)

Range: [0, 1]. 0 = full agreement, 0.5 = equal-weight disagreement.

2.4 Trend Confidence

confidence = clamp(0.3 × F_count + 0.3 × C_avg + 0.4 × A_agreement − P_contradiction, 0, 1)

Component	Formula
F_count (source count)	min(N_unique / 15, 0.8)
C_avg (extraction confidence)	mean of extraction confidences
A_agreement (signal agreement)	fraction_same_direction × min(1, log₂(N_unique + 1) / log₂(8))
P_contradiction	contradiction_score × 0.4

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2B. Probabilistic Trend Assembly (Feature-Flagged)

Source: services/aggregation/worker.py, services/aggregation/bayesian.py Active when: probabilistic_scoring_enabled = true

2B.1 Bayesian Posterior Accumulation

L_t = Σ(W_combined_i × sentiment_i)                    (log-likelihood)
P_bull = σ(L_t) = 1 / (1 + e^(−L_t))                  (bullish probability)
α = 1 + W_bull       (W_bull = Σ W_combined for positive signals)
β = 1 + W_bear       (W_bear = Σ W_combined for negative signals)
C_bayesian = 1 − 4αβ / (α + β)²                       (Bayesian confidence)
H = −P_bull·log₂(P_bull) − (1−P_bull)·log₂(1−P_bull)  (Shannon entropy)

Uninformative prior (no signals): P_bull=0.5, α=1, β=1, C=0, H=1.0.

2B.2 Entropy-Based Direction

Condition	Direction
H > 0.9	Mixed
P_bull > 0.65	Bullish
P_bull < 0.35	Bearish
otherwise	Neutral

2B.3 Bayesian Trend Confidence

confidence = clamp(0.5 × C_bayesian + 0.25 × F_count + 0.25 × C_avg_credibility − P_contradiction, 0, 1)

Component	Formula
C_bayesian	1 − 4αβ/(α+β)² from Beta posterior
F_count	min(N_unique_sources / 15, 0.8)
C_avg_credibility	mean credibility weight across active signals
P_contradiction	contradiction_entropy × regime.contradiction_penalty_multiplier

2B.4 Weighted Disagreement Entropy (Contradiction)

Source: services/aggregation/contradiction.py

f_pos = W_positive / (W_positive + W_negative)
f_neg = 1 − f_pos
H_contradiction = −f_pos·log₂(f_pos) − f_neg·log₂(f_neg)
contradiction_score = H_contradiction × min(1.0, (W_pos + W_neg) / W_threshold)

W_threshold default = 5.0. Returns 0.0 when only one direction exists.

2B.5 Regime Detection

Source: services/aggregation/regime.py

R = sign(EMA_20 − EMA_100)          (trend indicator)
V_r = σ_20 / σ_100                  (volatility ratio)

Condition	Regime	Threshold	Contradiction Mult
V_r > 1.5	Panic	±0.10	0.4
R ≠ 0 AND V_r < 1.2	Trend-following	±0.15	0.4
R = 0 AND V_r < 1.0	Mean-reversion	±0.20	0.4
otherwise	Uncertainty	±0.15	0.6

Falls back to Uncertainty when data < 100 days or σ = 0.

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3. Macro Impact Scoring (Layer 2)

Source: services/aggregation/interpolation.py

3.1 Overlap Components

Geographic overlap:

O_geo = Σ revenue_pct_r   for each event region r in company's revenue mix

Range: [0, 1]

Supply chain overlap:

O_supply = |event_regions ∩ supply_regions| / |supply_regions|

Commodity overlap:

O_commodity = |event_commodities ∩ company_commodities| / |company_commodities|

Sector overlap:

O_sector = 1.0 if company_sector ∈ event_affected_sectors, else 0.0

3.2 Raw Macro Impact Score

S_raw = W_severity × (0.35 × O_geo + 0.25 × O_supply + 0.25 × O_commodity + 0.15 × O_sector)

Severity weights:

Severity	W_severity
critical	1.0
high	0.75
moderate	0.5
low	0.25

3.3 Resilience Modifier

For international events, the raw score is adjusted by market position:

S_final = clamp(S_raw × R_tier, 0, 1)

Market Position Tier	R_tier
Global leader	0.70
Multinational	0.85
Regional	1.00
Domestic	1.20

For domestic-only events, R_tier = 1.0 regardless of tier.

3B. Multiplicative Macro Exposure (Probabilistic)

Active when: probabilistic_scoring_enabled = true

S_raw = W_severity × (1 − Π_k(1 − w_k × O_k))
     = W_severity × (1 − (1−0.35·O_geo)(1−0.25·O_supply)(1−0.25·O_commodity)(1−0.15·O_sector))

Zero overlap → 0.0. Max overlap (all 1.0) → severity × 0.689.

3B.1 Conditional Macro Integration

When both company and macro signals exist:

modifier = clamp(1 + M_macro × sign_alignment, 0.5, 1.5)
S_adjusted = S_company × modifier

sign_alignment = +1 (agree), −1 (disagree), 0 (neutral/mixed).

When only macro signals exist: additive fallback with weight 0.3. When only company signals exist: modifier = 1.0.

3.4 Macro Impact Confidence

confidence = min(event_confidence × min(O_total + 0.3, 1.0), 1.0)

where O_total = O_geo + O_supply + O_commodity + O_sector.

3.5 Accelerated Staleness Decay

For short-term events older than 48 hours:

decay_standard = e^(−0.693 × t_age_hours / t_half_hours)     (t_half default = 168h)
decay_accelerated = decay_standard × 0.5

3.6 Macro Signal as WeightedSignal

When merged into the aggregation engine:

impact_score_macro = macro_impact_score × W_macro       (W_macro = 0.3 default)
sentiment_value = +1 if positive, −1 if negative

Recency decay uses the global event's publication time.

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4. Competitive Signals (Layer 3)

4.1 Pattern Confidence

Source: services/aggregation/pattern_matcher.py

confidence = F_sample × 0.4 + F_consistency × 0.4 + F_recency × 0.2

Factor	Formula
F_sample	min(N_samples / 20, 1.0)
F_consistency	max(pct_bullish, pct_bearish)
F_recency	1.0 if age ≤ 7d; 0.7 if age ≤ 90d; 0.4 otherwise

Modifiers:

• Major corporate decision (m&a, earnings, legal): confidence × 1.3
• Insufficient data (N_samples < min_pattern_samples): cap at 0.25
• Stale data (age > staleness_window_days): confidence × staleness_decay_penalty

Lookback windows:

• Routine signals: 180 days
• Major corporate decisions: 365 days

4.2 Cross-Company Signal Strength

Source: services/aggregation/signal_propagation.py

S_competitive = clamp(S_pattern_avg × R_relationship × C_pattern × I_source, 0, 1)

Component	Description
S_pattern_avg	Average historical outcome strength [0, 1]
R_relationship	Relationship strength from competitor_relationships [0, 1]
C_pattern	Pattern confidence from §4.1
I_source	Source document's impact_score [0, 1]

Threshold gate: Skipped if R_relationship < propagation_strength_threshold (default 0.2).

4B. Graph-Distance Attenuation (Probabilistic)

Active when: probabilistic_scoring_enabled = true

S_transfer = S_source × ρ_historical × e^(−d_network)

Component	Description
S_source	Source signal strength
ρ_historical	90-day rolling Pearson correlation (default 0.3 same-sector, 0.1 cross-sector)
d_network	Shortest path in competitor graph (capped at 3)

No propagation when d_network > 3 (e^(−3) ≈ 0.05).

4.3 Competitive Signal as WeightedSignal

impact_score_competitive = S_competitive × W_competitive     (W_competitive = 0.2 default)
direction = majority historical outcome (bullish or bearish)

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5. Trend Projection

Source: services/aggregation/projection.py

5.1 Trend Momentum

momentum = S_current_signed − S_previous_signed

where S_signed = direction_sign × strength (bullish = +1, bearish = −1, neutral = 0).

When no previous data exists:

momentum = direction_sign × strength × 0.5

Range: [−1, 1]

5.2 Macro Decay Projection

For each active macro event projected forward by H days:

F_future = 2^(−(t_current + H) / t_half)
I_projected = macro_impact_score × F_future × W_severity

Decay half-lives:

Duration	t_half (days)
short_term	1.0
medium_term	7.0
long_term	30.0

Aggregate direction: bullish if W_pos > 1.2 × W_neg; bearish if W_neg > 1.2 × W_pos; mixed if both > 0.

5.3 Projection Blending

W_macro_blend = min(S_macro_projected × 0.4, 0.4)
W_company = 1.0 − W_macro_blend

S_blended = W_company × S_momentum_projected + W_macro_blend × S_macro_signed

Catalyst boost: min(N_catalysts × 0.02, 0.1) added to projected strength.

Projected confidence:

C_projected = C_base × 0.8 + min(S_macro × 0.15, 0.1)

Divergence detection: Flagged when projected direction ≠ current trend direction.

5B. Exponentially Weighted Momentum (Probabilistic)

Source: services/aggregation/projection.py Active when: probabilistic_scoring_enabled = true

M_t = Σ_{k=0}^{K-1} λ^k × ΔS_{t-k}     (λ = 0.7, K ≤ 10)
M_normalized = M_t / Σ_{k=0}^{K-1} λ^k   (range: [−1, 1])
M_adj = clamp(M_normalized / max(σ_20, 0.01), −2.0, 2.0)

Falls back to heuristic momentum when < 2 historical cycles available.

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6. Data Quality Suppression

Source: services/recommendation/suppression.py

6.1 Data Quality Score

Q = 0.4 × Q_confidence + 0.3 × Q_freshness + 0.3 × Q_coverage

Component	Formula
Q_confidence	min(C_avg_extraction / 0.8, 1.0)
Q_freshness	max(0, 1 − t_newest_hours / 168)
Q_coverage	(N_valid / N_total) × min(N_valid / 10, 1.0)

Suppression triggers (any one → informational only):

Check	Threshold
Avg extraction confidence	< 0.40
Evidence staleness	> 168 hours (7 days)
Source type diversity	< 1 distinct type
Extraction failure rate	> 50%
Valid document count	< 2
Data quality score	< 0.30

6.2 Safety Suppression

• Macro-only: If trend driven solely by macro signals with zero company evidence → forced informational
• Pattern-only: If trend driven solely by pattern/competitive signals with no company or macro support → forced informational

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7. Recommendation Eligibility

Source: services/recommendation/eligibility.py

7.1 Gate Checks (all must pass)

Check	Threshold
Confidence	≥ 0.35
Trend strength	≥ 0.10
Contradiction score	≤ 0.60
Evidence count	≥ 2
Direction	≠ neutral

7.2 Action Mapping

Condition	Action
Bullish AND strength ≥ 0.25	BUY
Bearish AND strength ≥ 0.25	SELL
Directional AND confidence ≥ 0.50	HOLD
Mixed or weak	WATCH

7.3 Mode Escalation

Mode	Requirements
live_eligible	confidence ≥ 0.70, contradiction ≤ 0.25, evidence ≥ 5
paper_eligible	confidence ≥ 0.50
informational	everything else (WATCH/HOLD always informational)

7B. Expected Value Gate (Probabilistic)

Active when: probabilistic_scoring_enabled = true

R_up = strength × σ_20 × √(horizon_days)
R_down = (1 − strength) × σ_20 × √(horizon_days)
EV = P_bull × R_up − (1 − P_bull) × R_down

Horizon window	horizon_days
intraday / 1d	1
7d	7
30d	30
90d	90

• EV > 0.005 (0.5% expected return): recommendation proceeds through existing gates
• EV ≤ 0.005: forced to informational mode regardless of confidence/strength
• All existing eligibility gates (§7.1) remain as additional requirements

7.4 Position Sizing

portfolio_pct = base + C_factor × S_factor × range × P_contradiction × P_evidence

Component	Formula	Default
base	base_portfolio_pct	0.01 (1%)
range	max_portfolio_pct − base_portfolio_pct	0.09 (9%)
C_factor	confidence_sizing_weight × confidence	0.8 × confidence
S_factor	0.5 + 0.5 × trend_strength	[0.5, 1.0]
P_contradiction	1 − (contradiction_penalty × contradiction_score)	penalty = 0.5
P_evidence	0.50 if evidence < 3; 0.75 if evidence < 5; 1.0 otherwise

Clamped to [base × 0.5, max_portfolio_pct].

Max loss percentage uses the same structure with base = 0.003 (0.3%) and max = 0.02 (2%).

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8. Trading Engine — Position Sizing

Source: services/trading/position_sizer.py

8.1 Base Allocation

raw_pct = (max_position_pct × 0.5) × (confidence / min_confidence) × multiplier
clamped_pct = min(raw_pct, max_position_pct)
dollar_amount = min(active_pool × clamped_pct, absolute_position_cap)

8.2 Correlation Reduction

ρ_avg = Σ(ρ_i × w_i) / Σ(w_i)     for existing positions

ρ_avg	Action
> 0.8	Reject order
0.5 < ρ_avg ≤ 0.8	Reduce: factor = 1 − (ρ_avg − 0.5) / 0.3
≤ 0.5	No reduction

8.3 Sector Exposure Reduction

available = max(max_sector_pct × active_pool − current_sector_exposure, 0)
dollar_amount = min(dollar_amount, available)

8.4 Diversification Bonus

If < 3 sectors held AND entering a new sector: dollar_amount × 1.2 (capped at max_position_pct).

8.5 Earnings Proximity

Days to earnings	Action
≤ 1	Reject
1–3	50% reduction
> 3	No adjustment

8.6 Portfolio Heat Check

heat_new = dollar_amount × atr_multiplier × 0.02
heat_max = max_portfolio_heat × active_pool

Reject if: heat_current + heat_new > heat_max

8.7 Share Rounding

shares = floor(dollar_amount / current_price)
final_dollar = shares × current_price

Reject if shares = 0.

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9. Stop-Loss and Take-Profit

Source: services/trading/stop_loss_manager.py

9.1 Initial Levels

stop_distance = ATR × M_atr
stop_loss = entry_price − stop_distance
take_profit = entry_price + stop_distance × R_reward_risk

Trade type	M_atr	R_reward_risk
Standard	risk_tier.stop_loss_atr_multiplier	risk_tier.reward_risk_ratio
Micro-trade	1.0	1.5

9.2 Dynamic Tightening

Condition	Effective multiplier
High-severity macro event	base × 0.5
Earnings within 3 days	base × 0.7
Portfolio heat > 80% of max	base × 0.7
Normal	base

9.3 Trailing Stop Activation

Activates when:

favorable_move = current_price − entry_price > 0.5 × (take_profit − entry_price)

Once active, stop-loss floor = entry_price (breakeven).

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10. Risk Management

10.1 Position Limits

Source: services/risk/engine.py

Limit	Default	Formula
Max position %	5%	position_value / portfolio_value ≤ 0.05
Max position value	$10,000	existing + new ≤ $10,000
Max shares/order	1,000	quantity ≤ 1,000
Max sector %	25%	sector_value / portfolio_value ≤ 0.25
Max daily loss %	2%
Max daily loss $	$1,000
Max daily trades	20	trade_count < 20

10.2 Order Clamping

Source: services/risk/engine.py — clamp_order_to_position_limits()

When a buy order exceeds position limits, instead of rejecting:

max_allowed_value = min(
    max_position_value − existing_value,
    max_position_pct × portfolio_value − existing_value
)
clamped_shares = min( floor(max_allowed_value / price_per_share), max_shares_per_order )

10.3 News Shock Lockout

Trigger: impact_score ≥ 0.80 for catalyst ∈ {earnings, legal, m_and_a} Duration: 60 minutes (configurable)

10.4 Symbol Cooldown

Duration: 15 minutes between trades on same symbol. Max concurrent positions per symbol: 1.

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11. Circuit Breaker

Source: services/trading/circuit_breaker.py

Trigger	Condition	Cooldown
Daily loss		daily_pnl
Single position	position_loss_pct > 0.15	48 hours
Volatility	≥ 3 stop-losses within 30-minute window	2 hours

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12. Risk Tier Auto-Adjustment

Source: services/trading/risk_tier_controller.py

Tiers: conservative → moderate → aggressive

Downgrade (any one triggers, drops one level):

• 30-day win rate < 40%
• Current drawdown > 15%

Upgrade (all must be true, raises one level):

• 30-day win rate > 55%
• Reserve pool > 20% of portfolio
• Current drawdown < 5%

---

13. Portfolio Rebalancing

Source: services/trading/rebalancer.py

13.1 Single-Stock Rebalancing

excess = market_value − max_position_pct × active_pool
sell_qty = min( floor(excess / current_price), position_quantity )

13.2 Sector Rebalancing

sector_excess = Σ(market_value_i) − max_sector_pct × active_pool

Sell from lowest-confidence positions first until excess is covered.

13.3 Max Positions Enforcement

excess_count = N_positions − max_positions

Sell entire lowest-confidence positions until count is within limit.

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Constants Summary

Constant	Value	Location
Confidence gate floor	0.20	scoring.py
Min recency weight	0.01	scoring.py
Credibility floor/ceiling	0.10 / 1.0	scoring.py
Novelty bonus max	0.25 (25%)	scoring.py
Volatility boost threshold	1.0 price units	scoring.py
Volatility boost max	0.30 (30%)	scoring.py
Volume surge threshold	50%	scoring.py
Volume surge boost	0.15 (15%)	scoring.py
Bullish/bearish threshold	±0.15	worker.py
Mixed threshold	contradiction > 0.10,	S
Macro signal weight	0.30	config.py
Competitive signal weight	0.20	config.py
Macro confidence threshold	0.40	interpolation.py
Staleness accelerated decay	0.50×	interpolation.py
Short-term staleness hours	48	interpolation.py
Pattern min samples	configurable	pattern_matcher.py
Major decision weight multiplier	1.3×	pattern_matcher.py
Routine lookback	180 days	pattern_matcher.py
Major decision lookback	365 days	pattern_matcher.py
Propagation strength threshold	0.20	signal_propagation.py
Data quality min score	0.30	suppression.py
Evidence staleness max	168 hours (7 days)	suppression.py
Recommendation min confidence	0.35	eligibility.py
Recommendation min strength	0.10	eligibility.py
Action strength threshold	0.25	eligibility.py
Live confidence threshold	0.70	eligibility.py
Paper confidence threshold	0.50	eligibility.py
Base portfolio allocation	1%	eligibility.py
Max portfolio allocation	10%	eligibility.py
Circuit breaker daily loss	5%	circuit_breaker.py
Circuit breaker single position	15%	circuit_breaker.py
Stop-loss cluster threshold	3 hits / 30 min	circuit_breaker.py
Tier downgrade win rate	< 40%	risk_tier_controller.py
Tier upgrade win rate	> 55%	risk_tier_controller.py
Tier upgrade max drawdown	< 5%	risk_tier_controller.py
Tier upgrade min reserve	> 20%	risk_tier_controller.py
Probabilistic pipeline
Sigmoid steepness (k)	5.0	scoring.py
Sigmoid midpoint (m)	0.5	scoring.py
Info gain lambda (λ)	0.3	scoring.py
Info gain max clamp	3.0	scoring.py
Default base rate	0.10	scoring.py
Adaptive decay impact scale	1.0	scoring.py
Adaptive decay surprise scale	1.0	scoring.py
Adaptive decay market scale	0.5	scoring.py
Regime return weight	0.15	scoring.py
Regime volume weight	0.10	scoring.py
Regime multiplier max	2.5	scoring.py
Source accuracy min samples	10	source_accuracy.py
Contradiction W_threshold	5.0	contradiction.py
EMA short period	20 days	regime.py
EMA long period	100 days	regime.py
Panic volatility ratio	> 1.5	regime.py
Trend-following vol ratio	< 1.2	regime.py
Mean-reversion vol ratio	< 1.0	regime.py
Panic threshold	±0.10	regime.py
Mean-reversion threshold	±0.20	regime.py
Uncertainty contradiction mult	0.6	regime.py
EW momentum decay (λ)	0.7	projection.py
EW momentum max lags (K)	10	projection.py
Volatility floor (σ min)	0.01	projection.py
Momentum clamp	±2.0	projection.py
EV threshold	0.005 (0.5%)	eligibility.py
Graph distance max	3	signal_propagation.py
Default correlation (same-sector)	0.3	signal_propagation.py
Default correlation (cross-sector)	0.1	signal_propagation.py