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GLOBAL MARKETS
Market Intelligence Active
100,000,000+
Securities
45+
Exchanges
182ms
Latency
▣
Portfolio Value
$0
Add a position to begin
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Active Signals
0
Awaiting universe scan
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Market Sentiment
Neutral
Calibrating…
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Top Opportunity
—
Universe-driven
Portfolio Performance
Sentiment
72 Bullish
AI INTELLIGENCE
Markets showing strong bullish momentum. Tech sector leading with NVDA and AAPL posting outsized gains driven by AI demand. Risk appetite elevated — monitor consolidation near resistance.
S&P 500 Sector Heatmap
Live performance · color-coded by return
Top AI Signals
Highest-conviction opportunities
LIVE · MULTI-FACTOR · AI-SCORED
AI Signals
Institutional-grade signals across 400+ securities, recalibrated every 15 minutes
GLOBAL MACRO DASHBOARD
STATIC
S&P 500
5,847
▲ +1.2%
NASDAQ
18,432
▲ +1.8%
10Y YIELD
4.28%
▼ -0.04%
VIX
16.2
▼ Low volatility
GOLD
$2,341
▲ +0.6%
OIL (WTI)
$78.4
▼ -0.9%
BTC
$67,234
▲ +3.1%
DXY
104.3
▼ -0.2%
Sector Performance
11 SPDR SECTORS · ETF-TRACKED
Technology
Software · Semiconductors · Cloud · Hardware
XLK
+2.4%
AAPLMSFTNVDAAVGOMETA
Healthcare
Pharma · Biotech · Medical Devices · Insurance
XLV
+0.8%
LLYUNHJNJABBVMRK
Financials
Banks · Insurance · Asset Managers · Exchanges
XLF
+1.1%
JPMBACWFCGSMS
Energy
Oil & Gas · Pipelines · Refining · Drilling
XLE
-0.9%
XOMCVXCOPEOGSLB
Consumer Disc.
E-Commerce · Autos · Restaurants · Retail
XLY
+0.3%
AMZNTSLAHDMCDNKE
Consumer Staples
Food · Beverages · Household · Tobacco
XLP
+0.2%
PGKOPEPWMTCOST
Utilities
Electric · Natural Gas · Water · Renewable
XLU
-1.2%
NEEDUKSODAEP
Industrials
Aerospace · Defense · Machinery · Transport
XLI
+0.5%
GECATHONRTXUPS
Materials
Chemicals · Metals · Mining · Packaging
XLB
-0.4%
LINAPDSHWFCXNEM
Real Estate
REITs · Data Centers · Commercial · Industrial
XLRE
-0.6%
AMTPLDEQIXSPGPSA
Communication
Internet · Media · Telecom · Streaming
XLC
+1.6%
GOOGLMETADISNFLXT
LIVE PRICING · AI ANALYSIS · RISK METRICS
Portfolio
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TOTAL VALUE
$0
Portfolio NAV
📊
TOTAL P&L
$0
Unrealized gain/loss
📅
DAY P&L
$0
Today's movement
Holdings
| TICKER | NAME | QTY | AVG COST | CURRENT | VALUE | P&L | SIGNAL |
|---|
AI Portfolio Analysis
⛛ ENTELLOQ AI
DIVERSIFICATION
Moderate
RISK EXPOSURE
Low-Med
TECH CONCENTRATION
High
Your portfolio is heavily concentrated in technology (68%). Consider diversifying into Healthcare, Financials, or Commodities to reduce sector risk. Current Sharpe Ratio: 1.84 — above average. Recommended action: Add 10–15% allocation to defensive sectors.
Add Position
Track a new stock position
TRANSACTION HISTORY · P&L TRACKING · AUDIT TRAIL
Trade Ledger
Complete record of all investment transactions with realized and unrealized P&L
TOTAL TRADES
—
BUY ORDERS
—
SELL ORDERS
—
NET P&L
—
Transaction History
| DATE | TICKER | TYPE | QTY | PRICE | TOTAL | P&L |
|---|
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QUANT ENTELLOQ AI
Hello! I'm your Quant Entelloq AI assistant — powered by Groq · LLaMA 3.3 70B ⚡
Ask me anything about markets, strategies, or your portfolio. I read the full context of your current page and portfolio to give you personalized analysis.
Try: "Analyze NVIDIA's growth outlook" · "What's driving tech stocks today?" · "Should I add gold exposure right now?"
Ask me anything about markets, strategies, or your portfolio. I read the full context of your current page and portfolio to give you personalized analysis.
Try: "Analyze NVIDIA's growth outlook" · "What's driving tech stocks today?" · "Should I add gold exposure right now?"
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Investment Parameters
Projection Results
Final Value
$—
Total Profit
$—
Total Return
—
CAGR (after fees)
—
Compound annual rate
Real Value (infl-adj)
$—
Today's purchasing power
After-Tax Value
$—
Post capital gains tax
Total Contributed
$—
Your money invested
Compound Growth
$—
Returns on returns
vs Benchmark
—
Fees Paid (total)
$—
Rule of 72 (doubling)
— yrs
Portfolio Setup
2008 Global Financial Crisis
-56.8%
Sep 2008 – Mar 2009 · 18 months recovery: 4 yr
2020 COVID-19 Crash
-33.9%
Feb – Mar 2020 · 5 weeks recovery: 5 mo
2000 Dot-com Bust
-49.1%
Mar 2000 – Oct 2002 · 30 months recovery: 7 yr
1987 Black Monday
-22.6%
Oct 19, 1987 · 1 day recovery: 2 yr
2022 Rate Hike Crash
-25.4%
Jan – Oct 2022 · 9 months recovery: 15 mo
1973 Oil Crisis
-48.2%
Jan 1973 – Oct 1974 · 22 months recovery: 8 yr
Crisis Impact Analysis
Select a crisis event and click Simulate →
Simulation Parameters
Configure parameters and run simulation →
Portfolio Allocation
Enter portfolio allocation and run stress tests →
Macro Shock Parameters
Macro Shocks
Fed Rate Hike (bps)
CPI Inflation Spike (%)
USD Strengthening (%)
Oil Price Change (%)
GDP Contraction (%)
Credit Spread Widening (bps)
Set macro shocks and model portfolio impact →
Strategy Configuration Bloomberg-grade engine · 9 strategies · full risk metrics
Benchmark: S&P 500 (~10% annual)
Performance Metrics
Total Return
—
CAGR
—
Sharpe Ratio
—
Sortino Ratio
—
Max Drawdown
—
Calmar Ratio
—
Win Rate
—
Profit Factor
—
Avg Win
—
Avg Loss
—
Total Trades
—
vs S&P 500
—
Equity Curve — vs benchmark
Drawdown
Monthly Returns Heatmap
Trade Log
| # | Dir | Entry Day | Exit Day | P&L ($) | Ret % |
|---|
AI-generated weekly intelligence reports
Quant Lab ⚗
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Quant Entelloq
Quant Research Coordinator
QUANT ENTELLOQ
Hi! I'm your quant research assistant. Tell me what you want to analyze in plain English and I'll pick the right model, run it, and explain what the results mean.Try: "How risky is a 55% win rate strategy?" or "Price a call on a $100 stock at $105 strike, 30 days, 25% vol."
Quick Start
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Analysis appears here
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⚡ Risk Analysis
🎯 Options Pricing
🎲 Monte Carlo
📈 Efficient Frontier
📐 Kelly Criterion
📊 GARCH Volatility
Inputs
Daily Returns (comma-separated) — leave blank for demo data
Risk-Free Rate (%/yr)
Benchmark Ann. Ret (%)
Performance Metrics
Ann. Return
—
Ann. Volatility
—
Sharpe Ratio
—
(rf-adjusted, annualized)
Sortino Ratio
—
(downside deviation)
Risk Metrics
Max Drawdown
—
Calmar Ratio
—
(CAGR / Max DD)
VaR 95%
—
(daily, 1-day horizon)
CVaR 95%
—
(expected shortfall)
Distribution + Factor Analysis
Omega Ratio
—
(gains / losses vs rf)
Beta (vs bench)
—
Alpha (ann.)
—
Skewness
—
Returns Distribution
Interpretation Guide
Sharpe > 1 is good, > 2 is excellent. Sortino penalizes only downside deviations — higher is better. VaR 95% means on 5% of days you can expect to lose at least this much. CVaR is the average loss in those worst 5% of days (also called Expected Shortfall). Calmar > 1 means the return justifies the drawdown risk. Omega > 1 means gains outweigh losses relative to the threshold. Negative skewness = fat left tail (crash risk). Kurtosis > 0 = fat tails vs normal.
Asset Inputs (Annual Expected Return % · Annual Volatility %)
Asset 1 Name
Exp. Return %
Volatility %
Asset 2 Name
Exp. Return %
Volatility %
Asset 3 Name
Exp. Return %
Volatility %
Asset 4 Name
Exp. Return %
Volatility %
Avg Pairwise Correlation
Simulated Portfolios
Modern Portfolio Theory
Each dot is a randomly weighted portfolio. The left-edge curve is the Efficient Frontier — no portfolio to the left of it is achievable. Min Variance = lowest possible risk. Max Sharpe = best risk-adjusted return (tangency portfolio). Real-world use: allocate capital along the frontier based on your risk tolerance. Diversification reduces portfolio volatility below the weighted average of individual asset vols when correlations < 1.
Black-Scholes Inputs
Spot Price (S)
Strike Price (K)
Days to Expiry
Risk-Free Rate (%)
Implied Vol (%)
Option Type
Option Premium
—
Call: —
Put: —
d1: —
d2: —
The Greeks
Delta (Δ)
—
Sensitivity to spot price
Gamma (Γ)
—
Rate of delta change
Theta (Θ)
—
Daily time decay ($)
Vega (ν)
—
Sensitivity per 1% vol
Rho (ρ)
—
Sensitivity per 1% rate
P&L at Expiration
Greeks Interpretation
Delta: How much the option price moves per $1 move in spot. Call delta 0–1, put delta −1–0. Gamma: How fast delta changes — high near expiry and ATM. Theta: Time decay per day — options lose value as time passes (negative for buyers). Vega: Value change per 1% increase in implied volatility — long options benefit from vol spikes. Rho: Interest rate sensitivity — minor for short-dated options.
Simulation Parameters
Initial Investment ($)
Ann. Return (%)
Ann. Volatility (%)
Time Horizon (yrs)
Monthly DCA ($)
Simulations
Percentile Outcomes (Final Portfolio Value)
P10 (Bear)
—
Worst 10% of outcomes
P25
—
P50 (Median)
—
P75
—
P90 (Bull)
—
Best 10% of outcomes
Prob. of Loss
—
Prob. 2× Money
—
Geometric Brownian Motion Simulation
Each path simulates one possible future using GBM: dS = μS·dt + σS·dW. The cone shows probabilistic spread — not a prediction. The median (P50) is the most likely outcome; the fan represents uncertainty that grows with time. Long-term investing reduces risk of loss as compounding overcomes short-term volatility. Results assume constant parameters — real markets have regime changes, tail events, and correlation breaks.
Returns Input
GARCH(1,1) Parameters
ω (omega)
—
Long-run variance floor
α (alpha)
—
ARCH effect (shock impact)
β (beta)
—
GARCH effect (persistence)
α + β (persistence)
—
<1 = stationary
Volatility Statistics
Long-Run Vol (ann.)
—
Current Cond. Vol
—
Shock Half-Life
—
days for vol shock to halve
10-Day Vol Forecast
5-Day Forecast
—
10-Day Forecast
—
GARCH Model Explained
GARCH(1,1) models time-varying volatility: σ²ₜ = ω + α·ε²ₜ₋₁ + β·σ²ₜ₋₁. Alpha captures how quickly vol reacts to shocks (ARCH effect). Beta measures persistence — how long high vol lasts. α + β close to 1 means volatility is highly persistent (common in equity markets). The half-life tells you how many days it takes for a volatility shock to decay by half. Long-run vol is where volatility reverts over time. Practical use: VaR, option pricing, risk management, position sizing.
Returns Input
Rolling Window (days)
Distribution Statistics
Daily Mean Return
—
Daily Std Dev
—
Skewness
—
<0 = left-tailed (crash risk)
Excess Kurtosis
—
>0 = fat tails vs normal
Mean Reversion + Normality
Jarque-Bera Stat
—
Normality Test
—
MR Half-Life
—
(Ornstein-Uhlenbeck)
ACF Lag-1 / Lag-5
— / —
Rolling Z-Score
Autocorrelation Function (ACF)
Statistical Concepts
Skewness: Negative = crash risk (more extreme left tails). Kurtosis: Positive excess kurtosis = more extreme events than a normal distribution predicts — standard risk models will underestimate tail risk. Jarque-Bera: Tests for normality — reject if > 5.99 (p<0.05). Mean Reversion Half-Life: From the Ornstein-Uhlenbeck model — how many days for a deviation from mean to halve. Low = fast mean reversion, useful for pair trading. ACF: Autocorrelation — bars outside the yellow band indicate significant serial correlation (predictability).
Asset Universe (8 major asset classes)
Using empirically derived correlations based on 5-year historical data (2019–2024).
Market Regime
Correlation Heatmap
Diversification Analysis
Avg Pairwise Corr
—
Diversification Score
—
(100 = fully uncorrelated)
Highest Corr Pair
—
Lowest Corr Pair
—
Correlation in Portfolio Construction
Correlations near +1 mean assets move together — no diversification benefit. Correlations near 0 provide maximum diversification. Negative correlations (bonds vs stocks in normal regimes) act as portfolio hedges. Warning: Correlations are not stable — they spike toward 1 during market crises (correlation breakdown), exactly when diversification is most needed. The crisis regime shows how correlations shift during stress events. Always stress-test correlation assumptions.
Position Sizing Calculator
Kelly Criterion gives the theoretically optimal fraction of capital to risk on each trade to maximize long-run geometric growth.
Mode
Win Rate (%)
Avg Win ($)
Avg Loss ($)
Ann. Expected Return (%)
Ann. Volatility (%)
Portfolio Size ($)
Max Position Cap (%)
Kelly Fractions
Full Kelly
—
Theoretical optimum
Half Kelly (recommended)
—
50% of full — safer in practice
Quarter Kelly
—
Conservative sizing
Edge per Trade
—
Expected value / trade
Dollar Amounts (Portfolio: $100,000)
Full Kelly Position
—
Half Kelly Position
—
Quarter Kelly Position
—
Capped Position
—
Growth Analysis
Kelly Criterion — Theory & Practice
Discrete Kelly: f* = (b·p − q) / b, where b = Win/Loss ratio, p = win rate, q = 1 − p. A negative result means the strategy has negative expected value — don't trade it.
Continuous Kelly: f* = μ / σ² (log-optimal portfolio fraction). Derived from maximizing E[ln(1 + f·r)].
Caution: Full Kelly maximizes long-run growth but causes extreme volatility — drawdowns of 50%+ are common. Half Kelly (f*/2) is the professional standard: it achieves ~75% of maximum growth with far less variance. Over-betting (f > Kelly) is strictly worse than under-betting in expectation. Kelly assumes accurate probability estimates — overconfidence leads to ruin.
Continuous Kelly: f* = μ / σ² (log-optimal portfolio fraction). Derived from maximizing E[ln(1 + f·r)].
Caution: Full Kelly maximizes long-run growth but causes extreme volatility — drawdowns of 50%+ are common. Half Kelly (f*/2) is the professional standard: it achieves ~75% of maximum growth with far less variance. Over-betting (f > Kelly) is strictly worse than under-betting in expectation. Kelly assumes accurate probability estimates — overconfidence leads to ruin.
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