neural_engine.py

01alphaScore = Σ(factorWeights[i] × zScores[i])

02volatility = sqrt(Σ((returns[i] - μ)²) / n)

03sharpeRatio = (E[R] - Rf) / σ(R)

04EMA[t] = α × Price[t] + (1-α) × EMA[t-1]

05zscore = (x - μ) / σ

06TVL_rate = dTVL/dt / TVL

07sentiment = NER ⊗ polarity_scores

08liquidityDepth = Σ(bids[i].volume + asks[i].volume)

09orderflow = tanh(Δvolume_buy - Δvolume_sell)

10spread = (ask - bid) / midpoint × 10000 # bps

11swaps → temporal_clusters → anomaly_score

12VaR₉₅ = μ - 1.645σ

13arbitrage_signal = max(0, cross_exchange_delta - fees)

14β = Cov(asset, market) / Var(market)

15kalman_gain = P⁻ Hᵀ(HPᵀH + R)⁻¹

16momentum = (price[t] / price[t-k]) - 1

17LSTM_state = tanh(Wₓxₜ + Wₕhₜ₋₁ + b)

18risk_parity = w[i] = (1/σ[i]) / Σ(1/σ[j])

19drawdown = (peak - current) / peak

20correlation_matrix = ρᵢⱼ = Cov(Xᵢ,Xⱼ) / (σᵢσⱼ)

21gamma_exposure = Σ(Γᵢ × OIᵢ × spotPrice²)

22funding_rate = (mark_price - index) / 8h

23impermanent_loss = 2√(price_ratio) / (1+price_ratio) - 1

24order_toxicity = signed_volume / sqrt(volume²)

25microstructure_noise = Var(observed) - Var(true)

01alphaScore = Σ(factorWeights[i] × zScores[i])

02volatility = sqrt(Σ((returns[i] - μ)²) / n)

03sharpeRatio = (E[R] - Rf) / σ(R)

04EMA[t] = α × Price[t] + (1-α) × EMA[t-1]

05zscore = (x - μ) / σ

06TVL_rate = dTVL/dt / TVL

07sentiment = NER ⊗ polarity_scores

08liquidityDepth = Σ(bids[i].volume + asks[i].volume)

09orderflow = tanh(Δvolume_buy - Δvolume_sell)

10spread = (ask - bid) / midpoint × 10000 # bps

11swaps → temporal_clusters → anomaly_score

12VaR₉₅ = μ - 1.645σ

13arbitrage_signal = max(0, cross_exchange_delta - fees)

14β = Cov(asset, market) / Var(market)

15kalman_gain = P⁻ Hᵀ(HPᵀH + R)⁻¹

16momentum = (price[t] / price[t-k]) - 1

17LSTM_state = tanh(Wₓxₜ + Wₕhₜ₋₁ + b)

18risk_parity = w[i] = (1/σ[i]) / Σ(1/σ[j])

19drawdown = (peak - current) / peak

20correlation_matrix = ρᵢⱼ = Cov(Xᵢ,Xⱼ) / (σᵢσⱼ)

21gamma_exposure = Σ(Γᵢ × OIᵢ × spotPrice²)

22funding_rate = (mark_price - index) / 8h

23impermanent_loss = 2√(price_ratio) / (1+price_ratio) - 1

24order_toxicity = signed_volume / sqrt(volume²)

25microstructure_noise = Var(observed) - Var(true)

01alphaScore = Σ(factorWeights[i] × zScores[i])

02volatility = sqrt(Σ((returns[i] - μ)²) / n)

03sharpeRatio = (E[R] - Rf) / σ(R)

04EMA[t] = α × Price[t] + (1-α) × EMA[t-1]

05zscore = (x - μ) / σ

06TVL_rate = dTVL/dt / TVL

07sentiment = NER ⊗ polarity_scores

08liquidityDepth = Σ(bids[i].volume + asks[i].volume)

09orderflow = tanh(Δvolume_buy - Δvolume_sell)

10spread = (ask - bid) / midpoint × 10000 # bps

11swaps → temporal_clusters → anomaly_score

12VaR₉₅ = μ - 1.645σ

13arbitrage_signal = max(0, cross_exchange_delta - fees)

14β = Cov(asset, market) / Var(market)

15kalman_gain = P⁻ Hᵀ(HPᵀH + R)⁻¹

16momentum = (price[t] / price[t-k]) - 1

17LSTM_state = tanh(Wₓxₜ + Wₕhₜ₋₁ + b)

18risk_parity = w[i] = (1/σ[i]) / Σ(1/σ[j])

19drawdown = (peak - current) / peak

20correlation_matrix = ρᵢⱼ = Cov(Xᵢ,Xⱼ) / (σᵢσⱼ)

21gamma_exposure = Σ(Γᵢ × OIᵢ × spotPrice²)

22funding_rate = (mark_price - index) / 8h

23impermanent_loss = 2√(price_ratio) / (1+price_ratio) - 1

24order_toxicity = signed_volume / sqrt(volume²)

25microstructure_noise = Var(observed) - Var(true)

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Signal Strength83.27

α(21) ↑ 0.42 • vol ↓ 3.1%

Market Microstructure12.56

orderflow z=1.8 • spread 4.2bps

Factor Radarβ 0.62

value • momentum • on-chain • liquidity • risk • sentiment

Anomaly Detection

On-chain Insights

Alpha Score

DeFi Liquidity Scan