🌌 FinTech Agent OS: Quantitative Multi-Asset Financial Intelligence & Overfitting Audit Platform

Institutional-Grade AI-Driven Strategy Engineering, Autonomous Agent Swarm, Quantum Optimization & Cryptographic Verification Platform

🌐 Live Production Deployments & Access Links

Environment	Platform	URL	Status
Web Application UI	Vercel	https://fintechos.vercel.app	`🟢 LIVE ONLINE`
Backend REST & WS API	Render	https://fintechos.onrender.com	`🟢 LIVE ONLINE`
Interactive API Manual	FastAPI Swagger	https://fintechos.onrender.com/docs	`🟢 LIVE ONLINE`

📜 Executive Summary & Product Vision

FinTech Agent OS is a state-of-the-art quantitative finance operating system engineered to bridge the gap between high-level natural language strategy formulation, rigorous mathematical backtesting, quantum portfolio optimization, and institutional-grade overfitting auditing.

⚠️ The Core Industry Problem: Backtest Overfitting & Selection Bias

In quantitative finance, over 90% of backtested strategies fail when deployed live. Quantitative researchers often fall into the trap of Data Snooping, P-Hacking, and Backtest Overfitting — tuning moving average periods, RSI thresholds, and signal parameters until historical returns appear extraordinary. These "curve-fitted" strategies capture random market noise rather than true predictive alpha, leading to catastrophic capital losses in live execution.

🛡️ The FinTech Agent OS Solution: Validation-First Architecture

FinTech Agent OS functions as an impartial scientific judge. It eliminates self-deception and backtest overfitting through a 10-node agentic validation workflow:

🔒 Atomic 30% Cryptographic Holdout Vault: Locks the final 24 months of market data into an un-peekable vault.
⛓️ Write-Ahead SHA-256 Audit Ledger: Cryptographically hashes every trial attempt into an append-only ledger, tracking total attempts (N_eff) to calculate the Deflated Sharpe Ratio (DSR).
🎲 Stationary Block Bootstrap (500+ Iterations): Resamples historical returns using Politis & Romano block bootstrap to generate non-parametric 95% confidence intervals.
🤖 Adversarial AI Skeptic Agent: Red-teams backtests against survivorship bias, regime sensitivity, and transaction cost decay.
⚛️ Quantum QUBO Portfolio Engine: Formulates asset allocation as Quadratic Unconstrained Binary Optimization for classical simulated annealing and quantum annealers.

🏗️ 10-Node Agentic Pipeline Architecture

graph TD
    A["1. NLP Prompt Parser<br/>(Natural Language ➔ Module Spec)"] --> B["2. Data Partition Service<br/>(70% Dev / 30% Holdout Vault)"]
    B --> C["3. Indicator & Signal Engine<br/>(SMA, EMA, RSI, Bollinger, HMM)"]
    C --> D["4. Vectorized Backtester<br/>(t+1 Open Fills, Slippage Model)"]
    D --> E["5. Financial Metrics Engine<br/>(Sharpe, Sortino, VaR, MaxDD)"]
    E --> F["6. Stationary Block Bootstrap<br/>(500 Iterations ➔ 95% CIs)"]
    F --> G["7. Robustness & Regime Grid<br/>(3x3 Parameter Heatmap + Fee Ladder)"]
    G --> H["8. Write-Ahead Audit Ledger<br/>(MongoDB Atlas + SHA-256 Hash Chain)"]
    H --> I["9. Adversarial AI Skeptic<br/>(Red-Teaming & Overfitting Audit)"]
    I --> J["10. Deterministic Verdict Engine<br/>(VERIFIED / OVERFIT / REJECTED)"]

💻 Backend Architecture: Modules, Submodules & Formulas

The backend is built with Python 3.11+ and FastAPI, engineered with modularity, mathematical precision, and high performance.

1. `app/analytics` — Quantitative Analytics Core

📄 `metrics.py` — Financial & Risk Metric Engine

Calculates institutional performance and risk metrics from daily return series R_t:

Annualized Return (CAGR):

$$\text{CAGR} = \left( \prod_{t=1}^{T} (1 + R_t) \right)^{\frac{252}{T}} - 1$$

Sharpe Ratio:

$$\text{Sharpe} = \frac{\mathbb{E}[R_p - R_f]}{\sigma_p} \times \sqrt{252}$$

(where R_f is risk-free rate, σ_p is annualized standard deviation of excess returns.)

Sortino Ratio:

$$\text{Sortino} = \frac{\mathbb{E}[R_p - R_f]}{\sigma_d} \times \sqrt{252}$$

(where σ_d is downside semi-deviation.)

Calmar Ratio:

$$\text{Calmar} = \frac{\text{CAGR}}{|\text{Maximum Drawdown}|}$$

Maximum Drawdown (MDD):

$$\text{MDD} = \max_{t \in [1, T]} \left( \frac{\max_{s \le t} P_s - P_t}{\max_{s \le t} P_s} \right)$$

Value at Risk (VaR - 95% / 99% Historical & Parametric):

$$\text{VaR}{\alpha}(R) = - Q{1-\alpha}(R) = - \inf { r \in \mathbb{R} : P(R \le r) \ge 1-\alpha }$$

Conditional Value at Risk (CVaR / Expected Shortfall):

$$\text{CVaR}{\alpha}(R) = -\mathbb{E}[R \mid R \le -\text{VaR}{\alpha}(R)]$$

📄 `engine.py` — Vectorized & Event-Driven Backtest Simulator

Order Execution Protocol: Signals generated at bar t are filled at the opening price of bar t+1 to eliminate lookahead bias.
Slippage & Market Impact Model:

$$\text{Fill Price} = P_{t+1, \text{open}} \times \left(1 + \text{sign}(\text{side}) \cdot \left( \frac{\text{Slippage Bps}}{10000} + \gamma \sqrt{\frac{V_{\text{order}}}{V_{\text{daily}}}} \right) \right)$$

Transaction Fee Model: Deducts fixed bps per trade execution (Commission Bps / 10000).

📄 `robustness.py` — Overfitting Diagnostics & Bootstrap Engine

Stationary Block Bootstrap (Politis & Romano 1994): Resamples return series using pseudo-random block sizes drawn from a geometric distribution with mean block length L = 20:

$$P(L=k) = (1-p)^{k-1} p, \quad p = 1/20$$

Deflated Sharpe Ratio (DSR - Bailey & López de Prado 2014): Adjusts observed Sharpe ratio S_hat for trial variance σ_S^2 and total logged trials N_eff:

$$\text{DSR} = Z \left( \frac{(\hat{S} - S_0) \sqrt{T-1}}{\sqrt{1 - \gamma_3 \hat{S} + \frac{\gamma_4 - 1}{4} \hat{S}^2}} \right)$$

(where γ_3 is skewness, γ_4 is kurtosis, and S_0 is the expected maximum Sharpe under null.)

📄 `calibration.py` — Null Universe & Walk-Forward Optimizer

1,000 Empirical Null Universes: Generates synthetic phase-scrambled time series to benchmark strategies against random chance.
Walk-Forward Efficiency Ratio (WFER):

$$\text{WFER} = \frac{\text{Sharpe}{\text{Out-of-Sample}}}{\text{Sharpe}{\text{In-Sample}}}$$

2. `app/analytics/strategy_modules` — Quant Strategy Module Library

Each quant module implements a standardized base protocol interface (BaseStrategyModule in base.py) registered in registry.py.

Module File	Class Name	Category	Mathematical Logic / Formula
sma_crossover.py	`SMACrossoverModule`	Trend	Long when `SMA_fast(t) > SMA_slow(t)`, Short/Cash otherwise.
bollinger_bands.py	`BollingerBandsModule`	Mean Reversion	Bands: `μ_N ± k · σ_N`. Buy when `P_t < Lower`, Sell when `P_t > Upper`.
rsi_oscillator.py	`RSIOscillatorModule`	Momentum	`RSI = 100 - 100 / (1 + EMA(Gain, n) / EMA(Loss, n))`. Oversold < 30, Overbought > 70.
macd_momentum.py	`MACDMomentumModule`	Momentum	`MACD = EMA_12 - EMA_26`, `Signal = EMA_9(MACD)`. Long on bullish crossover.
zscore_mean_reversion.py	`ZScoreMeanReversionModule`	Stat Arb	`Z_t = (P_t - μ_w) / σ_w`. Long when `Z_t < -2.0`, Short when `Z_t > +2.0`.
atr_sizing.py	`ATRSizingModule`	Volatility Sizing	`Position Size = (Capital × Risk %) / (k · ATR_n(t))`.
ichimoku.py	`IchimokuCloudModule`	Trend	Tenkan `(H9 + L9)/2`, Kijun `(H26 + L26)/2`, Senkou Span A/B Kumo cloud boundaries.
supertrend.py	`SupertrendModule`	Trend Following	`Upper/Lower = (H+L)/2 ± Multiplier × ATR_n`. Trailing stop trend lock.
vwap_execution.py	`VWAPExecutionModule`	Execution	`VWAP_t = Σ(P_i · V_i) / Σ(V_i)`. Benchmark execution & intra-day mean reversion.
pairs_trading.py	`PairsTradingModule`	Cointegration	Spread `S_t = P_A - β · P_B`. OLS cointegration β estimation & band trading.
momentum_rotation.py	`MomentumRotationModule`	Cross-Asset	Ranks universe assets by N-month momentum score and allocates capital to top K.
ml_regime.py	`MLRegimeModule`	Machine Learning	Gaussian Hidden Markov Model (HMM) 3-state classifier (Bull, Bear, Sideways/High-Vol).
var_cvar.py	`VaRCVaRModule`	Tail Risk	Historical & Cornish-Fisher VaR/CVaR risk engine for dynamic position deleveraging.
monte_carlo.py	`MonteCarloModule`	Stochastic Sim	Geometric Brownian Motion `dS_t = μ S_t dt + σ S_t dW_t` 1,000-path simulation.
portfolio_optimization.py	`PortfolioOptimizationModule`	Asset Allocation	Markowitz Mean-Variance (`min w^T Σ w`), Hierarchical Risk Parity (HRP), Min-Var.

3. `app/agents` — Multi-Agent Intelligence Swarm & LLM Inference Engine

Featherless.ai LLM Inference Integration: Strategy formulation, prompt parsing, and assistant reasoning are powered by the Featherless.ai API (https://api.featherless.ai/v1) using the state-of-the-art unsloth/Llama-3.3-70B-Instruct model specified in .env (FEATHERLESS_MODEL=unsloth/Llama-3.3-70B-Instruct).
📄 strategy_agent.py: Natural language prompt parser and strategy dynamic graph synthesizer. Translates user prompts like "Build an ATR position-sized Bollinger Band strategy on Gold" into fully wired multi-module JSON DAG pipelines.
📄 skeptic.py: Adversarial Red-Team Agent. Analyzes backtest results for signs of curve-fitting, lookahead leakage, unrealistic liquidity assumptions, and regime fragility.
📄 reporter.py: Institutional Reporter Agent. Generates compliant Federal Reserve SR 11-7 Model Risk Governance Documentation tear-sheets.
📄 factory.py: Multi-agent orchestrator that manages parallel execution loops and state transitions.

4. `app/audit` & `app/db` — MongoDB Persistence & Immutable Audit Engine

📄 mongo.py & strategy_store.py: Asynchronous MongoDB Atlas persistence database (driven by Motor & PyMongo) maintaining all historical strategy runs, user activity logs, prompts, pipelines, metrics, and AI reasoning.
📄 verdict.py: Deterministic 2-Phase Verification Engine:
- Phase A: Minimum Detectable Effect (MDE) Statistical Power analysis (>50%), selection-aware p-value check (p < 0.10).
- Phase B: Atomic Holdout evaluation. Checks whether out-of-sample Sharpe ratio degrades by more than 35% relative to in-sample performance.
📄 ledger.py: Cryptographic SHA-256 Hash Chain ledger stored in MongoDB ensuring tamper-proof audit trails for compliance.

5. `app/quantum` — Quantum QUBO Portfolio Optimization

📄 qubo_formulator.py: Converts continuous Markowitz portfolio selection into a Quadratic Unconstrained Binary Optimization (QUBO) Hamiltonian for Quantum Annealers (e.g. D-Wave) and Classical Simulated Annealing:

$$H(x) = - \sum_{i=1}^N \mu_i x_i + \gamma \sum_{i=1}^N \sum_{j=1}^N \sigma_{ij} x_i x_j + \lambda \left( \sum_{i=1}^N x_i - K \right)^2$$

(where x_i in {0, 1} indicates inclusion of asset i, γ is risk aversion factor, and λ penalizes deviation from cardinality constraint K.)

6. `app/data` — Multi-Asset Data Pipelines

📄 asset_loader.py: Fetches real-time market OHLCV data from Yahoo Finance (yfinance) across Crypto (BTC-USD, ETH-USD), Commodities (GC=F Gold, CL=F Crude Oil), Equities (NVDA, AAPL, SPY), and Forex (EURUSD=X), with fallback synthetic GBM data generators.
📄 elliptic_loader.py & 📄 amlsim_loader.py: Loaders for Bitcoin graph ML transaction datasets (Elliptic & AMLSim) for anti-money laundering and illicit transaction graph analysis.

🎨 Frontend Architecture: Pages, Visualizations & Graphs

The web application is built with Next.js 14, React 18, TypeScript, and Vanilla CSS / Custom Design System styled in dark mode and warm gold/terracotta accents.

🖥️ Screen 1: AI Strategy Builder & Execution Dashboard (`/` - page.tsx)

Natural Language Prompt Console: Real-time prompt input with instant AI synthesis.
Wired Pipeline Drawer: Visual representation of active strategy modules (e.g., Bollinger + ATR + HMM).
Interactive Backtest Recharts Canvas:
- Equity Curve Chart: Plots Portfolio Cumulative Equity vs Benchmark Asset.
- Drawdown Depth Plot: Visualizes percentage underwater periods over time.
Performance Summary Metric Grid: Displays Total Return, Sharpe Ratio, Sortino Ratio, Max Drawdown, Win Rate, and Total Trades.
Strategy Store History Drawer: Displays past persisted strategy runs with instant reload capabilities.

🏛️ Screen 2: Markets Hub & Bloomberg Terminal (`/markets` & `/assets` - page.tsx)

BloombergTickerRibbon.tsx: Scrolling live multi-asset ticker bar with price changes and volatility badges.
BloombergChart.tsx: Institutional candlestick and line price chart featuring volume overlays, moving averages, and RSI indicator panels.
MarketsAtAGlance.tsx & MarketsBoard.tsx: High-density market overview table categorizing Crypto, Commodities, Indices, and Equities.
MarketsMegaMenu.tsx: Navigation menu for asset universe selection.

🔬 Screen 3: Strategy Lab Canvas (`/research` - page.tsx)

Drag-and-Drop Module DAG Canvas: Multi-module pipeline composer connecting signal generators, risk sizing, and ML regime filters.
Parameter Tuning Drawer: Real-time sliders for lookback windows, std dev multipliers, and risk fractions.

📊 Screen 4: Robustness & Market Regimes (`/robustness` - page.tsx)

3x3 Parameter Sensitivity Heatmap: Grid mapping Sharpe ratio stability across neighboring lookback parameters.
Transaction Fee Ladder: Bar chart showing strategy decay under 0 to 20 bps slippage and commission costs.
2x2 Market Regime Matrix: Evaluates performance breakdown across Bull vs Bear and High Vol vs Low Vol market states.
Atomic Holdout Reveal Vault: Controlled one-time reveal button to validate out-of-sample data.

🛡️ Screen 5: Audit & Compliance Suite (`/audit` - page.tsx)

SHA-256 Cryptographic Ledger Timeline: Displays immutable hash chain sequence for all backtest attempts.
AI Skeptic Critique Panel: Red-teaming analysis highlighting over-fitting risks and structural flaws.
Deterministic Verdict Status: Displays VERIFIED, OVERFIT, or REJECTED verdict badge based on statistical power and holdout integrity.

🎲 Screen 6: Calibration & Null Universe Lab (`/calibration` - page.tsx)

Empirical Null Distribution Chart: Plots strategy Sharpe ratio against 1,000 synthetic null universes.
False Discovery Rate (FDR) Controller: Interactive p-value threshold calibration tool.

📐 Complete Mathematical Reference Guide

Metric / Model	Mathematical Formulation	Description
Sharpe Ratio	`S = (E[R_p - R_f] / σ_p) × √252`	Risk-adjusted return relative to total volatility.
Sortino Ratio	`S_d = (E[R_p - R_f] / σ_d) × √252`	Risk-adjusted return relative to downside volatility.
Calmar Ratio	`C = CAGR / \	Max Drawdown\
Deflated Sharpe Ratio (DSR)	`DSR = Z((S_hat - S_0)√(T-1) / √(1 - γ_3 S_hat + (γ_4-1)/4 S_hat^2))`	Sharpe ratio discounted for selection bias and trial variance.
Historical VaR (95%)	`VaR_0.95 = - Percentile(R, 5%)`	Maximum expected loss at 95% confidence level.
Expected Shortfall (CVaR)	`CVaR_α = - E[R \	R ≤ -VaR_α]`
HMM Market Regime	`P(S_t = j \	S_{t-1} = i) = A_{ij}, y_t ~ N(μ_j, Σ_j)`
QUBO Portfolio Hamiltonian	`H(x) = - μ^T x + γ x^T Σ x + λ (1^T x - K)^2`	Binary optimization for asset selection under risk & cardinality limits.
Stationary Block Bootstrap	`P(L=k) = (1-p)^{k-1} p, p = 1/L_bar`	Resampling returns with random block sizes to retain temporal dependence.

🔮 Vision for Further Development: Future Roadmap

Based on our architectural vision, the next development phases will focus on expanding execution capabilities, quantum integration, and deep learning for anti-fraud:

graph LR
    P1["Phase 1: Live Brokerage Execution Adapters"] --> P2["Phase 2: Decentralized Quantum Cloud Execution"]
    P2 --> P3["Phase 3: Graph Neural Networks (GNN) AML Engine"]
    P3 --> P4["Phase 4: Autonomous Multi-Agent Risk Committee"]

🚀 Phase 1: Live Brokerage & Order Routing Adapters

Alpaca & Interactive Brokers (IBKR) FIX Protocol Integration: Transition from simulated backtesting to direct paper/live trading via REST & WebSocket order routing.
Real-time Order Book Dynamics: Incorporate L2/L3 order book market depth, bid-ask spread impact, and order flow imbalance signals.

⚛️ Phase 2: Native Quantum Hardware Cloud Integration

D-Wave Leap & IBM Qiskit Connectors: Connect qubo_formulator.py to real D-Wave Advantage quantum annealers and IBM Quantum QPUs via cloud APIs.
Quantum Approximate Optimization Algorithm (QAOA): Implement QAOA circuits for constrained multi-period portfolio rebalancing.

🕸️ Phase 3: Graph Neural Network (GNN) Anti-Money Laundering Engine

Temporal Graph Convolutional Networks (T-GCN): Utilize elliptic_loader.py and amlsim_loader.py to train PyTorch Geometric GNN models for real-time Bitcoin transaction illicit node classification.
Sub-graph Anomaly Alerts: Flag high-risk wallet clusters and mixer transactions directly on the Markets dashboard.

🤖 Phase 4: Autonomous Multi-Agent Risk Committee

Continuous Portfolio Monitoring: Deploy a background worker swarm (RiskCommittee) that continuously evaluates live market volatility, HMM regime switches, and VaR limit breaches.
Automated Circuit Breakers: Trigger automated position de-leveraging and hedge execution when tail risk metrics exceed pre-set risk limits.

⚡ Quick Start & Development Setup

1. Prerequisites

Python: 3.11 or higher (uv package manager recommended)
Node.js: 18.x or higher (npm / pnpm)

2. Launch Backend API Server

cd backend
uv run uvicorn app.main:app --reload --port 8000

API Documentation: Access Swagger UI at http://localhost:8000/docs

3. Launch Frontend Web Application

cd frontend
npm run dev

Web Application: Access live interface at http://localhost:3000

FinTech Agent OS — Engineered with scientific rigor, mathematical precision, and state-of-the-art AI.

FinTechOS

🌌 FinTech Agent OS: Quantitative Multi-Asset Financial Intelligence & Overfitting Audit Platform

🌐 Live Production Deployments & Access Links

📜 Executive Summary & Product Vision

⚠️ The Core Industry Problem: Backtest Overfitting & Selection Bias

🛡️ The FinTech Agent OS Solution: Validation-First Architecture

🏗️ 10-Node Agentic Pipeline Architecture

💻 Backend Architecture: Modules, Submodules & Formulas

1. `app/analytics` — Quantitative Analytics Core

📄 `metrics.py` — Financial & Risk Metric Engine

📄 `engine.py` — Vectorized & Event-Driven Backtest Simulator

📄 `robustness.py` — Overfitting Diagnostics & Bootstrap Engine

📄 `calibration.py` — Null Universe & Walk-Forward Optimizer

2. `app/analytics/strategy_modules` — Quant Strategy Module Library

3. `app/agents` — Multi-Agent Intelligence Swarm & LLM Inference Engine

4. `app/audit` & `app/db` — MongoDB Persistence & Immutable Audit Engine

5. `app/quantum` — Quantum QUBO Portfolio Optimization

6. `app/data` — Multi-Asset Data Pipelines

🎨 Frontend Architecture: Pages, Visualizations & Graphs

🖥️ Screen 1: AI Strategy Builder & Execution Dashboard (`/` - page.tsx)

🏛️ Screen 2: Markets Hub & Bloomberg Terminal (`/markets` & `/assets` - page.tsx)

🔬 Screen 3: Strategy Lab Canvas (`/research` - page.tsx)

📊 Screen 4: Robustness & Market Regimes (`/robustness` - page.tsx)

🛡️ Screen 5: Audit & Compliance Suite (`/audit` - page.tsx)

🎲 Screen 6: Calibration & Null Universe Lab (`/calibration` - page.tsx)

📐 Complete Mathematical Reference Guide

🔮 Vision for Further Development: Future Roadmap

🚀 Phase 1: Live Brokerage & Order Routing Adapters

⚛️ Phase 2: Native Quantum Hardware Cloud Integration

🕸️ Phase 3: Graph Neural Network (GNN) Anti-Money Laundering Engine

🤖 Phase 4: Autonomous Multi-Agent Risk Committee

⚡ Quick Start & Development Setup

1. Prerequisites

2. Launch Backend API Server

3. Launch Frontend Web Application

Built With

Updates

🌌 FinTech Agent OS: Quantitative Multi-Asset Financial Intelligence & Overfitting Audit Platform

🌐 Live Production Deployments & Access Links

📜 Executive Summary & Product Vision

⚠️ The Core Industry Problem: Backtest Overfitting & Selection Bias

🛡️ The FinTech Agent OS Solution: Validation-First Architecture

🏗️ 10-Node Agentic Pipeline Architecture

💻 Backend Architecture: Modules, Submodules & Formulas

1. app/analytics — Quantitative Analytics Core

📄 metrics.py — Financial & Risk Metric Engine

📄 engine.py — Vectorized & Event-Driven Backtest Simulator

📄 robustness.py — Overfitting Diagnostics & Bootstrap Engine

📄 calibration.py — Null Universe & Walk-Forward Optimizer

2. app/analytics/strategy_modules — Quant Strategy Module Library

3. app/agents — Multi-Agent Intelligence Swarm & LLM Inference Engine

4. app/audit & app/db — MongoDB Persistence & Immutable Audit Engine

5. app/quantum — Quantum QUBO Portfolio Optimization

6. app/data — Multi-Asset Data Pipelines

🎨 Frontend Architecture: Pages, Visualizations & Graphs

🖥️ Screen 1: AI Strategy Builder & Execution Dashboard (/ - page.tsx)

🏛️ Screen 2: Markets Hub & Bloomberg Terminal (/markets & /assets - page.tsx)

🔬 Screen 3: Strategy Lab Canvas (/research - page.tsx)

📊 Screen 4: Robustness & Market Regimes (/robustness - page.tsx)

🛡️ Screen 5: Audit & Compliance Suite (/audit - page.tsx)

🎲 Screen 6: Calibration & Null Universe Lab (/calibration - page.tsx)

📐 Complete Mathematical Reference Guide

🔮 Vision for Further Development: Future Roadmap

🚀 Phase 1: Live Brokerage & Order Routing Adapters

⚛️ Phase 2: Native Quantum Hardware Cloud Integration

🕸️ Phase 3: Graph Neural Network (GNN) Anti-Money Laundering Engine

🤖 Phase 4: Autonomous Multi-Agent Risk Committee

⚡ Quick Start & Development Setup

1. Prerequisites

2. Launch Backend API Server

3. Launch Frontend Web Application

Built With

Updates

1. `app/analytics` — Quantitative Analytics Core

📄 `metrics.py` — Financial & Risk Metric Engine

📄 `engine.py` — Vectorized & Event-Driven Backtest Simulator

📄 `robustness.py` — Overfitting Diagnostics & Bootstrap Engine

📄 `calibration.py` — Null Universe & Walk-Forward Optimizer

2. `app/analytics/strategy_modules` — Quant Strategy Module Library

3. `app/agents` — Multi-Agent Intelligence Swarm & LLM Inference Engine

4. `app/audit` & `app/db` — MongoDB Persistence & Immutable Audit Engine

5. `app/quantum` — Quantum QUBO Portfolio Optimization

6. `app/data` — Multi-Asset Data Pipelines

🖥️ Screen 1: AI Strategy Builder & Execution Dashboard (`/` - page.tsx)

🏛️ Screen 2: Markets Hub & Bloomberg Terminal (`/markets` & `/assets` - page.tsx)

🔬 Screen 3: Strategy Lab Canvas (`/research` - page.tsx)

📊 Screen 4: Robustness & Market Regimes (`/robustness` - page.tsx)

🛡️ Screen 5: Audit & Compliance Suite (`/audit` - page.tsx)

🎲 Screen 6: Calibration & Null Universe Lab (`/calibration` - page.tsx)