Initial commit: fx_quant_engine and options_quant_engine scaffold

2026-07-27 18:37:47 +00:00 · 2026-03-30 17:20:45 +05:30
commit bfd32becc7
114 changed files with 4974 additions and 0 deletions
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+name: fx-quant-engine-ci
+
+on:
+  push:
+    paths:
+      - "fx_quant_engine/**"
+      - "config/**"
+      - "tests/**"
+      - "scripts/**"
+      - "pyproject.toml"
+  pull_request:
+    paths:
+      - "fx_quant_engine/**"
+      - "config/**"
+      - "tests/**"
+      - "scripts/**"
+      - "pyproject.toml"
+  workflow_dispatch:
+
+jobs:
+  lint-test-build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e '.[dev]'
+
+      - name: Lint
+        run: ruff check .
+
+      - name: Test
+        run: pytest
+
+      - name: Generate sample run artifact
+        run: python scripts/run_engine.py
+
+      - name: Upload run artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: fx-engine-runs
+          path: examples/runs/*.json
+          if-no-files-found: warn
+          retention-days: 30
@@ -0,0 +1,44 @@
+name: fx-quant-engine-release
+
+on:
+  push:
+    tags:
+      - "v*"
+  workflow_dispatch:
+
+jobs:
+  build-release-artifacts:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install build tools
+        run: |
+          python -m pip install --upgrade pip
+          pip install build twine
+
+      - name: Build wheel and sdist
+        run: python -m build
+
+      - name: Verify artifacts
+        run: twine check dist/*
+
+      - name: Upload release distribution artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: fx-quant-engine-dist
+          path: dist/*
+          retention-days: 90
+
+      - name: Create GitHub release
+        uses: softprops/action-gh-release@v2
+        with:
+          files: dist/*
+          generate_release_notes: true
@@ -0,0 +1,32 @@
+# macOS
+.DS_Store
+
+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+*.so
+*.egg-info/
+*.egg
+.pytest_cache/
+.coverage
+htmlcov/
+.mypy_cache/
+.ruff_cache/
+
+# Virtual environments
+.venv/
+venv/
+
+# Build artifacts
+build/
+dist/
+
+# Local run artifacts
+examples/runs/
+options_quant_engine/examples/runs/
+
+# IDE
+.vscode/
+.idea/
@@ -0,0 +1,193 @@
+# fx_quant_engine
+
+A professional, modular, explainable, and production-ready starter repository for an FX quant signal engine focused on India-first constraints with global extensibility.
+
+## What This Engine Does
+
+- Ingests FX/macro/rate data from multiple pluggable adapters
+- Uses configurable source priority and fallback logic per asset
+- Engineers economically meaningful features through a registry-based pipeline
+- Detects interpretable market regimes
+- Generates directional and relative-value FX signals
+- Separates signal strength from confidence
+- Applies risk overlays and outputs action recommendations
+- Supports evaluation and realistic backtest scaffolding
+- Exports integration hooks for cross-engine multi-asset platforms
+
+## Asset Universe
+
+Primary INR pairs:
+
+- USDINR
+- EURINR
+- GBPINR
+- JPYINR
+
+Secondary G10 pairs:
+
+- EURUSD
+- GBPUSD
+- USDJPY
+- AUDUSD
+- USDCAD
+- USDCHF
+
+## Repository Structure
+
+- fx_quant_engine/ingestion: adapters + fallback router
+- fx_quant_engine/preprocessing: data cleaning/alignment
+- fx_quant_engine/features: registry + modular feature pipeline
+- fx_quant_engine/regime: explainable regime detection
+- fx_quant_engine/models: model scoring and extension points
+- fx_quant_engine/signals: directional/RV signal + confidence engine
+- fx_quant_engine/risk: risk overlays and action mapping
+- fx_quant_engine/evaluation: signal quality diagnostics
+- fx_quant_engine/backtest: no-lookahead pair/portfolio simulation
+- fx_quant_engine/outputs: machine-readable + trader-readable payloads
+- fx_quant_engine/integration: cross-engine score exports
+- config: YAML-driven behavior
+- scripts: runnable examples
+- tests: unit tests
+- examples: sample payloads and run artifacts
+
+## Data Adapter Architecture
+
+Base interface:
+
+```python
+class BaseDataAdapter:
+    def fetch_price_data(self, asset, start, end):
+        pass
+
+    def fetch_macro_data(self, key, start, end):
+        pass
+
+    def fetch_rate_data(self, asset, start, end):
+        pass
+
+    def health_check(self):
+        pass
+```
+
+Included adapters:
+
+- BreezeAdapter (ICICI Breeze)
+- ZerodhaAdapter (optional via config)
+- NSEAdapter
+- RBIAdapter
+- FreeFXAdapter
+- MockAdapter
+
+Live API wiring uses strict vendor-specific parsers with schema validation and supports:
+
+- FXE_HTTP_TIMEOUT_SEC
+- FXE_HTTP_MAX_ATTEMPTS
+- FXE_HTTP_BACKOFF_SEC
+- BREEZE_LIVE_ENABLED, BREEZE_BASE_URL, BREEZE_API_KEY, BREEZE_API_SECRET
+- ZERODHA_LIVE_ENABLED, ZERODHA_BASE_URL, ZERODHA_API_KEY, ZERODHA_ACCESS_TOKEN
+- NSE_LIVE_ENABLED, NSE_BASE_URL
+- RBI_LIVE_ENABLED, RBI_BASE_URL
+
+Router safeguards include adapter-level circuit breakers and source cooldown windows.
+
+## Config-Driven Files
+
+- config/universe.yaml
+- config/data_sources.yaml
+- config/features.yaml
+- config/regimes.yaml
+- config/models.yaml
+- config/risk.yaml
+- config/output.yaml
+
+## Quickstart
+
+1. Create and activate a Python 3.10+ environment.
+1. Install package and dev dependencies:
+
+```bash
+pip install -e '.[dev]'
+```
+
+1. Run tests:
+
+```bash
+pytest
+```
+
+1. Run engine example:
+
+```bash
+python scripts/run_engine.py
+```
+
+The script saves artifacts to examples/runs.
+
+## Signal Output Schema
+
+Each signal includes:
+
+- direction
+- signal strength
+- confidence (separate module)
+- regime context
+- expected volatility
+- key drivers
+- risk flags
+- recommended action
+- position sizing multiplier
+
+See examples/sample_signal.json.
+
+## Relative Value Spread Model
+
+Relative-value generation uses:
+
+- explicit hedge ratio estimation from aligned log prices
+- spread z-score component
+- pair-level momentum and carry differentials
+- risk-overlayed RV signal output with `hedge_ratio` and `spread_zscore`
+
+## Release Workflow
+
+Automated release workflow is available in `.github/workflows/release.yml`.
+
+- triggers on version tags like `v0.1.0`
+- builds wheel + source distribution
+- validates artifacts via twine
+- publishes GitHub release assets with retention policy
+
+## Build Phases Coverage
+
+Phase 1:
+
+- repo structure
+- config system
+- ingestion + preprocessing
+- basic features
+
+Phase 2:
+
+- regime detection
+- signal engine
+- confidence engine
+- outputs
+
+Phase 3:
+
+- risk layer
+- evaluation framework
+- backtesting scaffolding
+
+Phase 4 starter:
+
+- model extension hooks
+- integration hooks
+- tests
+- docs
+
+## Notes
+
+- No credentials are hardcoded.
+- Live API integration can be added by replacing adapter internals while preserving interfaces.
+- Mock pathways are included for deterministic testing and offline development.
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+enabled_sources:
+  breeze: true
+  zerodha: false
+  nse: true
+  rbi: true
+  freefx: true
+  mock: true
+
+source_priority:
+  default: [nse, rbi, freefx, mock]
+  g10: [freefx, mock]
+
+asset_source_map:
+  USDINR: [nse, breeze, rbi, mock]
+  EURINR: [nse, breeze, freefx, mock]
+  GBPINR: [nse, freefx, mock]
+  JPYINR: [nse, freefx, mock]
+  EURUSD: [freefx, mock]
+  GBPUSD: [freefx, mock]
+  USDJPY: [freefx, mock]
+  AUDUSD: [freefx, mock]
+  USDCAD: [freefx, mock]
+  USDCHF: [freefx, mock]
+
+reliability_tags:
+  breeze: medium
+  zerodha: medium
+  nse: high
+  rbi: high
+  freefx: medium
+  mock: low
+
+latency_tags_ms:
+  breeze: 350
+  zerodha: 250
+  nse: 500
+  rbi: 800
+  freefx: 700
+  mock: 1
+
+live_api:
+  breeze_live_enabled: false
+  zerodha_live_enabled: false
+  nse_live_enabled: false
+  rbi_live_enabled: false
+
+http_controls:
+  timeout_sec: 5
+  max_attempts: 3
+  backoff_sec: 0.5
+
+circuit_breaker:
+  failure_threshold: 3
+  cooldown_seconds: 120
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+features:
+  returns: true
+  moving_averages: true
+  breakouts: true
+  trend_strength: true
+  momentum_multi_horizon: true
+  momentum_persistence: true
+  momentum_acceleration: true
+  mean_reversion_zscore: true
+  bollinger_position: true
+  realized_vol: true
+  atr_proxy: true
+  vol_regime_proxy: true
+  carry_proxy: true
+  usd_strength_score: true
+  risk_on_off_proxy: true
+  inr_crude_proxy: true
+  em_stress_proxy: true
+  inr_vol_clustering: true
+  rbi_intervention_proxy: true
+  cross_asset_bond_proxy: true
+  cross_asset_commodity_proxy: true
+  cross_asset_equity_proxy: true
+
+windows:
+  short: 5
+  medium: 20
+  long: 60
@@ -0,0 +1,22 @@
+directional:
+  model_weights:
+    trend: 0.35
+    momentum: 0.25
+    mean_reversion: 0.15
+    carry: 0.10
+    macro: 0.15
+
+relative_value:
+  lookback: 60
+  spread_zscore_threshold: 1.0
+  model_weights:
+    spread: 0.5
+    momentum_diff: 0.3
+    carry_diff: 0.2
+
+confidence:
+  agreement_weight: 0.25
+  regime_stability_weight: 0.20
+  data_quality_weight: 0.20
+  volatility_penalty_weight: 0.20
+  event_penalty_weight: 0.15
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+output:
+  include_dashboard_payload: true
+  include_trader_summary: true
+  precision: 4
+  engine_name: fx_quant_engine
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+trend:
+  slope_threshold: 0.0005
+  mr_band: 0.003
+
+volatility:
+  low_quantile: 0.3
+  high_quantile: 0.7
+  stress_quantile: 0.9
+
+dollar:
+  neutral_band: 0.2
+
+risk:
+  stress_vol_threshold: 0.75
+  risk_off_equity_threshold: -0.01
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+risk:
+  target_vol: 0.10
+  max_gross_exposure: 1.0
+  max_single_asset_exposure: 0.25
+  stressed_regime_multiplier: 0.5
+  illiquid_market_multiplier: 0.6
+  data_reliability_penalty:
+    high: 1.0
+    medium: 0.85
+    low: 0.6
+  market_hours:
+    inr_derivatives_open_utc: "03:45"
+    inr_derivatives_close_utc: "10:00"
@@ -0,0 +1,12 @@
+primary_inr_pairs:
+  - USDINR
+  - EURINR
+  - GBPINR
+  - JPYINR
+secondary_g10_pairs:
+  - EURUSD
+  - GBPUSD
+  - USDJPY
+  - AUDUSD
+  - USDCAD
+  - USDCHF
@@ -0,0 +1,28 @@
+{
+  "engine": "fx_quant_engine",
+  "timestamp": "2026-03-30T10:00:00",
+  "asset": "USDINR",
+  "signal_type": "directional",
+  "signal_direction": "long_usd",
+  "signal_strength": 0.74,
+  "confidence": 0.68,
+  "regime": {
+    "trend": "trend",
+    "volatility": "normal",
+    "dollar": "strengthening",
+    "risk": "risk_off",
+    "combined": "usd_dominant",
+    "stability": 0.71
+  },
+  "expected_volatility": 0.089,
+  "risk_flags": ["outside_primary_market_hours"],
+  "drivers": {
+    "trend": 0.51,
+    "momentum": 0.36,
+    "mean_reversion": -0.12,
+    "carry": 0.09,
+    "macro": -0.07
+  },
+  "recommended_action": "enter_small",
+  "position_sizing_multiplier": 0.65
+}
@@ -0,0 +1,5 @@
+"""FX Quant Engine package."""
+
+from fx_quant_engine.engine import FXQuantEngine
+
+__all__ = ["FXQuantEngine"]
@@ -0,0 +1,3 @@
+from fx_quant_engine.backtest.engine import BacktestEngine
+
+__all__ = ["BacktestEngine"]
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+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.schemas import BacktestResult
+
+
+class BacktestEngine:
+    def __init__(self, transaction_cost_bps: float = 1.5, slippage_bps: float = 1.0) -> None:
+        self.tc = transaction_cost_bps / 10000.0
+        self.slippage = slippage_bps / 10000.0
+
+    def simulate_pair(self, prices: pd.Series, signal_strength: pd.Series) -> pd.Series:
+        returns = prices.pct_change().fillna(0.0)
+        position = signal_strength.shift(1).fillna(0.0)  # no lookahead
+        traded = position.diff().abs().fillna(0.0)
+        net = position * returns - traded * (self.tc + self.slippage)
+        return net
+
+    def simulate_portfolio(self, pair_returns: dict[str, pd.Series]) -> BacktestResult:
+        if not pair_returns:
+            return BacktestResult({}, 0.0, 0.0, 0.0, {})
+
+        df = pd.DataFrame(pair_returns).fillna(0.0)
+        port = df.mean(axis=1)
+        equity = (1.0 + port).cumprod()
+        dd = equity / equity.cummax() - 1.0
+
+        pair_level = {k: float(v.mean() * 252.0) for k, v in pair_returns.items()}
+        metrics = {
+            "annualized_return": float(port.mean() * 252.0),
+            "annualized_vol": float(port.std() * np.sqrt(252.0)),
+            "sharpe": float((port.mean() / (port.std() + 1e-9)) * np.sqrt(252.0)),
+        }
+
+        return BacktestResult(
+            pair_level_returns=pair_level,
+            portfolio_return=float((equity.iloc[-1] - 1.0) if len(equity) else 0.0),
+            max_drawdown=float(dd.min() if len(dd) else 0.0),
+            turnover=float(df.diff().abs().sum().sum()),
+            metrics=metrics,
+        )
+
+    def walk_forward(self, prices: pd.Series, signal_strength: pd.Series, train_window: int = 120, test_window: int = 20) -> pd.Series:
+        out = []
+        idx = prices.index
+        i = train_window
+        while i < len(idx):
+            end = min(i + test_window, len(idx))
+            segment = self.simulate_pair(prices.iloc[:end], signal_strength.iloc[:end])
+            out.append(segment.iloc[i:end])
+            i += test_window
+        return pd.concat(out).sort_index() if out else pd.Series(dtype=float)
@@ -0,0 +1,214 @@
+from __future__ import annotations
+
+from datetime import datetime
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from fx_quant_engine.features.pipeline import FeaturePipeline
+from fx_quant_engine.ingestion.adapters import (
+    BreezeAdapter,
+    FreeFXAdapter,
+    MockAdapter,
+    NSEAdapter,
+    RBIAdapter,
+    ZerodhaAdapter,
+)
+from fx_quant_engine.ingestion.router import DataSourceRouter
+from fx_quant_engine.integration.hooks import IntegrationHooks
+from fx_quant_engine.models.ensemble import EnsembleModel
+from fx_quant_engine.models.relative_value import RelativeValueModel
+from fx_quant_engine.outputs.formatter import OutputFormatter
+from fx_quant_engine.preprocessing.cleaning import preprocess_market_data
+from fx_quant_engine.regime.engine import RegimeEngine
+from fx_quant_engine.risk.engine import RiskEngine
+from fx_quant_engine.schemas import SignalPayload
+from fx_quant_engine.signals.confidence import ConfidenceEngine
+from fx_quant_engine.signals.engine import SignalEngine
+from fx_quant_engine.utils.config import load_all_configs
+
+
+class FXQuantEngine:
+    def __init__(self, config_dir: str | Path = "config") -> None:
+        self.config = load_all_configs(config_dir)
+        self.adapters = {
+            "breeze": BreezeAdapter(),
+            "zerodha": ZerodhaAdapter(),
+            "nse": NSEAdapter(),
+            "rbi": RBIAdapter(),
+            "freefx": FreeFXAdapter(),
+            "mock": MockAdapter(),
+        }
+        self.router = DataSourceRouter(self.adapters, self.config["data_sources"])
+        self.features = FeaturePipeline(self.config["features"])
+        self.regimes = RegimeEngine(self.config["regimes"])
+        self.models = EnsembleModel(self.config["models"])
+        self.rv_model = RelativeValueModel(
+            lookback=int(self.config["models"].get("relative_value", {}).get("lookback", 60))
+        )
+        self.confidence = ConfidenceEngine(self.config["models"])
+        self.signals = SignalEngine(
+            engine_name=self.config["output"]["output"].get("engine_name", "fx_quant_engine")
+        )
+        self.risk = RiskEngine(self.config["risk"])
+        self.output = OutputFormatter(self.config["output"])
+        self.integration = IntegrationHooks()
+
+    def _asset_context(self, asset: str, start: datetime, end: datetime) -> dict[str, Any]:
+        px_res = self.router.fetch_price_data(asset, start, end)
+        macro_res = self.router.fetch_macro_data("macro_proxy", start, end)
+        rate_res = self.router.fetch_rate_data(asset, start, end)
+
+        if not px_res.success or px_res.data.empty:
+            raise RuntimeError(f"No price data available for {asset}: {px_res.error}")
+
+        df = px_res.data.join(macro_res.data, how="left").join(rate_res.data, how="left")
+        if f"{asset}_rate" in df.columns:
+            df["short_rate"] = df[f"{asset}_rate"]
+        df["usd_rate"] = 0.045
+        df["equity_proxy"] = df["close"].pct_change().rolling(5).mean().fillna(0.0)
+        df["crude_proxy"] = (1.0 + df["close"].pct_change().fillna(0.0)).cumprod()
+        df["emfx_proxy"] = df["close"].pct_change().fillna(0.0)
+
+        clean = preprocess_market_data(df)
+        feat = self.features.transform(clean)
+        regime = self.regimes.detect(feat)
+
+        return {
+            "price": px_res,
+            "macro": macro_res,
+            "rate": rate_res,
+            "clean": clean,
+            "features": feat,
+            "regime": regime,
+        }
+
+    def run_asset(self, asset: str, start: datetime, end: datetime) -> dict[str, Any]:
+        ctx = self._asset_context(asset, start, end)
+        px_res = ctx["price"]
+        feat = ctx["features"]
+        regime = ctx["regime"]
+        row = feat.iloc[-1]
+
+        score, model_drivers, model_agreement = self.models.directional_score(row)
+        feature_agreement = float((row > 0).mean())
+        data_quality = 1.0 if px_res.reliability == "high" else 0.8 if px_res.reliability == "medium" else 0.6
+        volatility = float(min(1.0, row.get("realized_vol", 0.0)))
+        event_risk = float(min(1.0, row.get("rbi_intervention_proxy", 0.0)))
+
+        conf = self.confidence.compute(
+            feature_agreement=feature_agreement,
+            model_agreement=model_agreement,
+            regime_stability=regime.stability,
+            data_quality=data_quality,
+            volatility=volatility,
+            event_risk=event_risk,
+        )
+
+        sig: SignalPayload = self.signals.directional_signal(
+            asset=asset,
+            feature_row=row,
+            regime=regime,
+            score=score,
+            confidence=conf,
+            drivers=model_drivers,
+            expected_volatility=float(row.get("realized_vol", 0.0)),
+        )
+        sig = self.risk.apply(
+            signal=sig,
+            regime=regime,
+            reliability=px_res.reliability,
+            liquidity_ok=True,
+            event_risk=event_risk,
+        )
+
+        payload = self.output.to_json_payload(sig)
+        summary = self.output.trader_summary(sig)
+        dashboard = self.output.dashboard_payload(sig)
+        hooks = self.integration.export_scores(feat, regime)
+
+        return {
+            "signal": payload,
+            "summary": summary,
+            "dashboard": dashboard,
+            "integration_hooks": hooks,
+            "source_usage": [u.__dict__ for u in self.router.usage_log],
+        }
+
+    def run_relative_value(self, asset_a: str, asset_b: str, start: datetime, end: datetime) -> dict[str, Any]:
+        ctx_a = self._asset_context(asset_a, start, end)
+        ctx_b = self._asset_context(asset_b, start, end)
+        feat_a = ctx_a["features"]
+        feat_b = ctx_b["features"]
+
+        rv = self.rv_model.generate(
+            asset_a=asset_a,
+            asset_b=asset_b,
+            prices_a=ctx_a["clean"]["close"],
+            prices_b=ctx_b["clean"]["close"],
+            features_a=feat_a,
+            features_b=feat_b,
+        )
+
+        regime = ctx_a["regime"] if ctx_a["regime"].stability >= ctx_b["regime"].stability else ctx_b["regime"]
+        driver_vals = [v for k, v in rv.drivers.items() if k != "hedge_ratio"]
+        signs = [np.sign(v) for v in driver_vals if abs(v) > 1e-12]
+        model_agreement = float(abs(sum(signs)) / len(signs)) if signs else 0.0
+        feature_agreement = float((feat_a.iloc[-1] - feat_b.iloc[-1] > 0).mean())
+
+        rel_map = {"high": 1.0, "medium": 0.8, "low": 0.6}
+        q_a = rel_map.get(ctx_a["price"].reliability, 0.6)
+        q_b = rel_map.get(ctx_b["price"].reliability, 0.6)
+        data_quality = float((q_a + q_b) / 2.0)
+
+        volatility = float(min(1.0, rv.expected_volatility))
+        event_risk = float(
+            min(
+                1.0,
+                (
+                    float(feat_a.iloc[-1].get("rbi_intervention_proxy", 0.0))
+                    + float(feat_b.iloc[-1].get("rbi_intervention_proxy", 0.0))
+                )
+                / 2.0,
+            )
+        )
+
+        conf = self.confidence.compute(
+            feature_agreement=feature_agreement,
+            model_agreement=model_agreement,
+            regime_stability=regime.stability,
+            data_quality=data_quality,
+            volatility=volatility,
+            event_risk=event_risk,
+        )
+
+        sig = self.signals.relative_value_signal(
+            long_asset=rv.long_asset,
+            short_asset=rv.short_asset,
+            regime=regime,
+            score=rv.score,
+            confidence=conf,
+            drivers=rv.drivers,
+            expected_volatility=rv.expected_volatility,
+        )
+        reliability = "high" if min(q_a, q_b) >= 1.0 else "medium" if min(q_a, q_b) >= 0.8 else "low"
+        sig = self.risk.apply(
+            signal=sig,
+            regime=regime,
+            reliability=reliability,
+            liquidity_ok=True,
+            event_risk=event_risk,
+        )
+
+        return {
+            "relative_value_pair": f"{asset_a}:{asset_b}",
+            "long": rv.long_asset,
+            "short": rv.short_asset,
+            "spread_score": rv.score,
+            "hedge_ratio": rv.hedge_ratio,
+            "spread_zscore": rv.spread_zscore,
+            "signal": self.output.to_json_payload(sig),
+            "summary": self.output.trader_summary(sig),
+            "dashboard": self.output.dashboard_payload(sig),
+        }
@@ -0,0 +1,3 @@
+from fx_quant_engine.evaluation.engine import Evaluator
+
+__all__ = ["Evaluator"]
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.schemas import EvaluationReport
+
+
+class Evaluator:
+    def evaluate(
+        self,
+        signals: pd.DataFrame,
+        forward_returns: pd.Series,
+        regimes: pd.Series,
+        feature_importance: dict[str, float],
+    ) -> EvaluationReport:
+        if signals.empty or forward_returns.empty:
+            return EvaluationReport(
+                generated_at=datetime.now(timezone.utc).isoformat(),
+                hit_rate=0.0,
+                avg_forward_return=0.0,
+                regime_performance={},
+                signal_decay={},
+                feature_importance=feature_importance,
+                drift_flags=["insufficient_data"],
+                calibration_score=0.0,
+            )
+
+        aligned = signals.join(forward_returns.rename("fwd"), how="inner")
+        aligned = aligned.join(regimes.rename("regime"), how="left").fillna("unknown")
+        pred = np.sign(aligned["signal_strength"] * aligned.get("signal_sign", 1.0))
+        realized = np.sign(aligned["fwd"])
+        hit_rate = float((pred == realized).mean())
+        avg_ret = float(aligned["fwd"].mean())
+
+        regime_perf = {
+            r: float(v)
+            for r, v in aligned.groupby("regime")["fwd"].mean().to_dict().items()
+        }
+
+        signal_decay = {
+            "1d": float(aligned["fwd"].mean()),
+            "5d": float(aligned["fwd"].rolling(5).mean().dropna().mean() if len(aligned) >= 5 else 0.0),
+            "20d": float(aligned["fwd"].rolling(20).mean().dropna().mean() if len(aligned) >= 20 else 0.0),
+        }
+
+        drift = []
+        if abs(avg_ret) < 1e-4:
+            drift.append("low_signal_edge")
+        if hit_rate < 0.48:
+            drift.append("hit_rate_deterioration")
+
+        conf = aligned.get("confidence", pd.Series(0.5, index=aligned.index))
+        calibration_score = float(np.clip(1.0 - abs(conf.mean() - hit_rate), 0.0, 1.0))
+
+        return EvaluationReport(
+            generated_at=datetime.now(timezone.utc).isoformat(),
+            hit_rate=hit_rate,
+            avg_forward_return=avg_ret,
+            regime_performance=regime_perf,
+            signal_decay=signal_decay,
+            feature_importance=feature_importance,
+            drift_flags=drift,
+            calibration_score=calibration_score,
+        )
@@ -0,0 +1,3 @@
+from fx_quant_engine.features.pipeline import FeaturePipeline
+
+__all__ = ["FeaturePipeline"]
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any
+
+import pandas as pd
+
+FeatureFn = Callable[[pd.DataFrame, dict[str, Any]], pd.Series]
+
+
+class FeatureRegistry:
+    def __init__(self) -> None:
+        self._features: dict[str, FeatureFn] = {}
+
+    def register(self, name: str, fn: FeatureFn) -> None:
+        if name in self._features:
+            raise ValueError(f"Feature already registered: {name}")
+        self._features[name] = fn
+
+    def get(self, name: str) -> FeatureFn:
+        return self._features[name]
+
+    def names(self) -> list[str]:
+        return list(self._features.keys())
+
+
+registry = FeatureRegistry()
@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.features.base import registry
+
+
+def _close(df: pd.DataFrame) -> pd.Series:
+    if "close" in df.columns:
+        return df["close"]
+    numeric = df.select_dtypes(include="number")
+    if numeric.empty:
+        return pd.Series(0.0, index=df.index)
+    return numeric.iloc[:, 0]
+
+
+def returns(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    return _close(df).pct_change().fillna(0.0)
+
+
+def moving_averages(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["medium"]
+    c = _close(df)
+    return (c / c.rolling(w).mean() - 1.0).fillna(0.0)
+
+
+def breakouts(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["long"]
+    c = _close(df)
+    high = c.rolling(w).max()
+    low = c.rolling(w).min()
+    rng = (high - low).replace(0.0, np.nan)
+    return ((c - low) / rng).fillna(0.5)
+
+
+def trend_strength(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["medium"]
+    r = returns(df, cfg)
+    return (r.rolling(w).mean() / (r.rolling(w).std() + 1e-9)).fillna(0.0)
+
+
+def momentum_multi_horizon(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    s = cfg["windows"]["short"]
+    m = cfg["windows"]["medium"]
+    l = cfg["windows"]["long"]
+    return ((c.pct_change(s) + c.pct_change(m) + c.pct_change(l)) / 3.0).fillna(0.0)
+
+
+def momentum_persistence(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return r.rolling(cfg["windows"]["short"]).apply(lambda x: float((x > 0).mean()), raw=False).fillna(0.5)
+
+
+def momentum_acceleration(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    m = momentum_multi_horizon(df, cfg)
+    return m.diff().fillna(0.0)
+
+
+def mean_reversion_zscore(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    w = cfg["windows"]["medium"]
+    m = c.rolling(w).mean()
+    s = c.rolling(w).std()
+    return ((c - m) / (s + 1e-9)).fillna(0.0)
+
+
+def bollinger_position(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    z = mean_reversion_zscore(df, cfg)
+    return (z / 2.0).clip(-1, 1)
+
+
+def realized_vol(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return (r.rolling(cfg["windows"]["medium"]).std() * np.sqrt(252)).fillna(0.0)
+
+
+def atr_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    return c.diff().abs().rolling(cfg["windows"]["short"]).mean().fillna(0.0)
+
+
+def vol_regime_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    vol = realized_vol(df, cfg)
+    return (vol / (vol.rolling(cfg["windows"]["long"]).mean() + 1e-9)).fillna(1.0)
+
+
+def carry_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    short_r = df.get("short_rate", pd.Series(0.05, index=df.index))
+    usd_r = df.get("usd_rate", pd.Series(0.045, index=df.index))
+    return (short_r - usd_r).fillna(0.0)
+
+
+def usd_strength_score(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return (-r.rolling(cfg["windows"]["medium"]).mean()).fillna(0.0)
+
+
+def risk_on_off_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    eq = df.get("equity_proxy", returns(df, cfg)).fillna(0.0)
+    vix = df.get("vix_proxy", realized_vol(df, cfg)).fillna(0.0)
+    return (eq - vix).fillna(0.0)
+
+
+def inr_crude_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    crude = df.get("crude_proxy", pd.Series(0.0, index=df.index))
+    return crude.pct_change().fillna(0.0)
+
+
+def em_stress_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    em = df.get("emfx_proxy", returns(df, cfg)).fillna(0.0)
+    return (-em.rolling(cfg["windows"]["medium"]).mean()).fillna(0.0)
+
+
+def inr_vol_clustering(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return r.abs().rolling(cfg["windows"]["short"]).mean().fillna(0.0)
+
+
+def rbi_intervention_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    z = mean_reversion_zscore(df, cfg)
+    return (z.abs() > 2.0).astype(float)
+
+
+def cross_asset_bond_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    y = df.get("bond_yield_proxy", pd.Series(0.0, index=df.index))
+    return y.diff().fillna(0.0)
+
+
+def cross_asset_commodity_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    g = df.get("gold_proxy", pd.Series(0.0, index=df.index))
+    o = df.get("oil_proxy", pd.Series(0.0, index=df.index))
+    return (g.pct_change().fillna(0.0) + o.pct_change().fillna(0.0)) / 2.0
+
+
+def cross_asset_equity_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    e = df.get("equity_proxy", pd.Series(0.0, index=df.index))
+    return e.pct_change().fillna(0.0)
+
+
+def register_builtin_features() -> None:
+    registry.register("returns", returns)
+    registry.register("moving_averages", moving_averages)
+    registry.register("breakouts", breakouts)
+    registry.register("trend_strength", trend_strength)
+    registry.register("momentum_multi_horizon", momentum_multi_horizon)
+    registry.register("momentum_persistence", momentum_persistence)
+    registry.register("momentum_acceleration", momentum_acceleration)
+    registry.register("mean_reversion_zscore", mean_reversion_zscore)
+    registry.register("bollinger_position", bollinger_position)
+    registry.register("realized_vol", realized_vol)
+    registry.register("atr_proxy", atr_proxy)
+    registry.register("vol_regime_proxy", vol_regime_proxy)
+    registry.register("carry_proxy", carry_proxy)
+    registry.register("usd_strength_score", usd_strength_score)
+    registry.register("risk_on_off_proxy", risk_on_off_proxy)
+    registry.register("inr_crude_proxy", inr_crude_proxy)
+    registry.register("em_stress_proxy", em_stress_proxy)
+    registry.register("inr_vol_clustering", inr_vol_clustering)
+    registry.register("rbi_intervention_proxy", rbi_intervention_proxy)
+    registry.register("cross_asset_bond_proxy", cross_asset_bond_proxy)
+    registry.register("cross_asset_commodity_proxy", cross_asset_commodity_proxy)
+    registry.register("cross_asset_equity_proxy", cross_asset_equity_proxy)
@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from fx_quant_engine.features.base import registry
+from fx_quant_engine.features.builtins import register_builtin_features
+
+
+class FeaturePipeline:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+        if not registry.names():
+            register_builtin_features()
+
+    def transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        feature_cfg = self.config.get("features", {})
+        out = pd.DataFrame(index=df.index)
+        for feature_name, enabled in feature_cfg.items():
+            if not enabled:
+                continue
+            if feature_name not in registry.names():
+                continue
+            out[feature_name] = registry.get(feature_name)(df, self.config)
+        return out.fillna(0.0)
@@ -0,0 +1,27 @@
+from fx_quant_engine.ingestion.adapters import (
+    BreezeAdapter,
+    FreeFXAdapter,
+    MockAdapter,
+    NSEAdapter,
+    RBIAdapter,
+    ZerodhaAdapter,
+)
+from fx_quant_engine.ingestion.base import BaseDataAdapter
+from fx_quant_engine.ingestion.credentials import APICredentials, load_credentials
+from fx_quant_engine.ingestion.http_client import HttpClient, RetryConfig
+from fx_quant_engine.ingestion.router import DataSourceRouter
+
+__all__ = [
+    "BaseDataAdapter",
+    "BreezeAdapter",
+    "ZerodhaAdapter",
+    "NSEAdapter",
+    "RBIAdapter",
+    "FreeFXAdapter",
+    "MockAdapter",
+    "DataSourceRouter",
+    "APICredentials",
+    "load_credentials",
+    "HttpClient",
+    "RetryConfig",
+]
@@ -0,0 +1,264 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+import os
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.ingestion.base import BaseDataAdapter
+from fx_quant_engine.ingestion.credentials import APICredentials, load_credentials
+from fx_quant_engine.ingestion.http_client import HttpClient, RetryConfig
+from fx_quant_engine.ingestion.parsers import (
+    BreezeResponseParser,
+    NSEResponseParser,
+    RBIResponseParser,
+    ZerodhaResponseParser,
+)
+
+
+def _mock_price_series(asset: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    steps = rng.normal(loc=0.0, scale=0.002, size=len(idx))
+    base = 80.0 if asset.endswith("INR") else 1.1
+    px = base * np.exp(np.cumsum(steps))
+    return pd.DataFrame({"close": px}, index=idx)
+
+
+def _mock_macro_series(key: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    values = rng.normal(loc=0.0, scale=1.0, size=len(idx))
+    return pd.DataFrame({key: values}, index=idx)
+
+
+def _mock_rate_series(asset: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    rate = 0.05 + rng.normal(loc=0.0, scale=0.002, size=len(idx))
+    return pd.DataFrame({f"{asset}_rate": rate}, index=idx)
+
+
+def _retry_config_from_env() -> RetryConfig:
+    return RetryConfig(
+        timeout_sec=float(os.getenv("FXE_HTTP_TIMEOUT_SEC", "5.0")),
+        max_attempts=int(os.getenv("FXE_HTTP_MAX_ATTEMPTS", "3")),
+        backoff_sec=float(os.getenv("FXE_HTTP_BACKOFF_SEC", "0.5")),
+    )
+
+
+def _live_enabled(env_key: str) -> bool:
+    return os.getenv(env_key, "false").lower() in {"1", "true", "yes", "on"}
+
+
+@dataclass
+class BreezeAdapter(BaseDataAdapter):
+    use_live: bool = False
+    base_url: str | None = None
+
+    def __post_init__(self) -> None:
+        self.use_live = self.use_live or _live_enabled("BREEZE_LIVE_ENABLED")
+        self.base_url = self.base_url or os.getenv("BREEZE_BASE_URL")
+        self.client = HttpClient(_retry_config_from_env())
+        self.parser = BreezeResponseParser()
+
+    def _creds(self) -> APICredentials:
+        return load_credentials("BREEZE", required=["API_KEY", "API_SECRET"], optional=["SESSION_TOKEN"])
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/price",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": str(creds.get("api_key") or "")},
+            )
+            return self.parser.parse_price(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=1)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/macro",
+                params={"key": key, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": str(creds.get("api_key") or "")},
+            )
+            return self.parser.parse_macro(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=2)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/rates",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": str(creds.get("api_key") or "")},
+            )
+            return self.parser.parse_rate(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=3)
+
+    def health_check(self) -> bool:
+        if not self.use_live:
+            return True
+        return bool(self.base_url)
+
+
+@dataclass
+class ZerodhaAdapter(BaseDataAdapter):
+    use_live: bool = False
+    base_url: str | None = None
+
+    def __post_init__(self) -> None:
+        self.use_live = self.use_live or _live_enabled("ZERODHA_LIVE_ENABLED")
+        self.base_url = self.base_url or os.getenv("ZERODHA_BASE_URL")
+        self.client = HttpClient(_retry_config_from_env())
+        self.parser = ZerodhaResponseParser()
+
+    def _creds(self) -> APICredentials:
+        return load_credentials("ZERODHA", required=["API_KEY", "ACCESS_TOKEN"])
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Zerodha live enabled but ZERODHA_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/instruments/historical",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={
+                    "X-Kite-Version": "3",
+                    "Authorization": (
+                        f"token {str(creds.get('api_key') or '')}:{str(creds.get('access_token') or '')}"
+                    ),
+                },
+            )
+            return self.parser.parse_price(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=4)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        raise NotImplementedError("Zerodha macro data not supported")
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        raise NotImplementedError("Zerodha rates not supported")
+
+    def health_check(self) -> bool:
+        if not self.use_live:
+            return True
+        return bool(self.base_url)
+
+
+class NSEAdapter(BaseDataAdapter):
+    def __init__(self, use_live: bool = False, base_url: str | None = None) -> None:
+        self.use_live = use_live or _live_enabled("NSE_LIVE_ENABLED")
+        self.base_url = base_url or os.getenv("NSE_BASE_URL", "https://www.nseindia.com")
+        self.client = HttpClient(_retry_config_from_env())
+        self.parser = NSEResponseParser()
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/historical/foCPV",
+                params={"symbol": asset, "from": start.strftime("%d-%m-%Y"), "to": end.strftime("%d-%m-%Y")},
+                headers={"User-Agent": "fx-quant-engine/0.1"},
+            )
+            return self.parser.parse_price(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=5)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/allIndices",
+                headers={"User-Agent": "fx-quant-engine/0.1"},
+            )
+            return self.parser.parse_macro(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=6)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/live-analysis-oi-spurts-underlyings",
+                headers={"User-Agent": "fx-quant-engine/0.1"},
+            )
+            return self.parser.parse_rate(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=7)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class RBIAdapter(BaseDataAdapter):
+    def __init__(self, use_live: bool = False, base_url: str | None = None) -> None:
+        self.use_live = use_live or _live_enabled("RBI_LIVE_ENABLED")
+        self.base_url = base_url or os.getenv("RBI_BASE_URL", "https://data.rbi.org.in")
+        self.client = HttpClient(_retry_config_from_env())
+        self.parser = RBIResponseParser()
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/exchange-rate",
+                params={"pair": asset, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return self.parser.parse_price(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=8)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/macro",
+                params={"series": key, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return self.parser.parse_macro(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=9)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/policy-rates",
+                params={"asset": asset, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return self.parser.parse_rate(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=10)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class FreeFXAdapter(BaseDataAdapter):
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_price_series(asset, start, end, seed=11)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_macro_series(key, start, end, seed=12)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_rate_series(asset, start, end, seed=13)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class MockAdapter(BaseDataAdapter):
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_price_series(asset, start, end, seed=42)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_macro_series(key, start, end, seed=43)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_rate_series(asset, start, end, seed=44)
+
+    def health_check(self) -> bool:
+        return True
@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from datetime import datetime
+
+import pandas as pd
+
+
+class BaseDataAdapter(ABC):
+    @abstractmethod
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def health_check(self) -> bool:
+        pass
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class APICredentials:
+    values: dict[str, str]
+
+    def get(self, key: str, default: str | None = None) -> str | None:
+        return self.values.get(key, default)
+
+
+def load_credentials(prefix: str, required: list[str], optional: list[str] | None = None) -> APICredentials:
+    optional = optional or []
+    payload: dict[str, str] = {}
+
+    missing: list[str] = []
+    for key in required:
+        env_key = f"{prefix}_{key}"
+        value = os.getenv(env_key)
+        if not value:
+            missing.append(env_key)
+            continue
+        payload[key.lower()] = value
+
+    for key in optional:
+        env_key = f"{prefix}_{key}"
+        value = os.getenv(env_key)
+        if value:
+            payload[key.lower()] = value
+
+    if missing:
+        raise RuntimeError(f"Missing required credentials: {', '.join(missing)}")
+    return APICredentials(values=payload)
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Any
+
+import requests
+
+
+@dataclass
+class RetryConfig:
+    timeout_sec: float = 5.0
+    max_attempts: int = 3
+    backoff_sec: float = 0.5
+
+
+class HttpClient:
+    def __init__(self, config: RetryConfig) -> None:
+        self.config = config
+
+    def get_json(
+        self,
+        url: str,
+        params: dict[str, Any] | None = None,
+        headers: dict[str, str] | None = None,
+    ) -> dict[str, Any]:
+        last_err = "unknown"
+        for attempt in range(1, self.config.max_attempts + 1):
+            try:
+                response = requests.get(url, params=params, headers=headers, timeout=self.config.timeout_sec)
+                response.raise_for_status()
+                data = response.json()
+                if not isinstance(data, dict):
+                    raise RuntimeError("Expected JSON object response")
+                return data
+            except Exception as exc:  # noqa: BLE001
+                last_err = str(exc)
+                if attempt < self.config.max_attempts:
+                    time.sleep(self.config.backoff_sec * attempt)
+
+        raise RuntimeError(
+            f"HTTP GET failed after {self.config.max_attempts} attempts for {url}: {last_err}"
+        )
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+
+import pandas as pd
+
+
+class BaseResponseParser:
+    def parse_price(self, payload: dict[str, Any], start: datetime, end: datetime) -> pd.DataFrame:
+        return _extract_price(payload, start, end)
+
+    def parse_macro(self, payload: dict[str, Any], key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _extract_series(payload, key, start, end)
+
+    def parse_rate(self, payload: dict[str, Any], column: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _extract_series(payload, column, start, end)
+
+
+class BreezeResponseParser(BaseResponseParser):
+    pass
+
+
+class ZerodhaResponseParser(BaseResponseParser):
+    pass
+
+
+class NSEResponseParser(BaseResponseParser):
+    pass
+
+
+class RBIResponseParser(BaseResponseParser):
+    pass
+
+
+def _extract_price(payload: dict[str, Any], start: datetime, end: datetime) -> pd.DataFrame:
+    records = payload.get("data")
+    if not isinstance(records, list) or not records:
+        raise RuntimeError("Invalid payload: 'data' must be a non-empty list")
+
+    df = pd.DataFrame(records)
+    if "timestamp" in df.columns:
+        df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True, errors="coerce")
+        df = df.dropna(subset=["timestamp"]).set_index("timestamp")
+    elif "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce")
+        df = df.dropna(subset=["date"]).set_index("date")
+    else:
+        raise RuntimeError("Invalid payload: missing timestamp/date column")
+
+    if "close" not in df.columns:
+        if "price" in df.columns:
+            df = df.rename(columns={"price": "close"})
+        else:
+            raise RuntimeError("Invalid payload: missing close/price column")
+
+    out = df[["close"]].sort_index()
+    start_ts = pd.Timestamp(start)
+    end_ts = pd.Timestamp(end)
+    start_ts = start_ts.tz_localize("UTC") if start_ts.tzinfo is None else start_ts.tz_convert("UTC")
+    end_ts = end_ts.tz_localize("UTC") if end_ts.tzinfo is None else end_ts.tz_convert("UTC")
+    clipped = out[(out.index >= start_ts) & (out.index <= end_ts)]
+    if clipped.empty:
+        raise RuntimeError("Invalid payload: no records in requested window")
+    return clipped
+
+
+def _extract_series(payload: dict[str, Any], key: str, start: datetime, end: datetime) -> pd.DataFrame:
+    records = payload.get("data")
+    if not isinstance(records, list) or not records:
+        raise RuntimeError("Invalid payload: 'data' must be a non-empty list")
+
+    df = pd.DataFrame(records)
+    if "timestamp" in df.columns:
+        df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True, errors="coerce")
+        df = df.dropna(subset=["timestamp"]).set_index("timestamp")
+    elif "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce")
+        df = df.dropna(subset=["date"]).set_index("date")
+    else:
+        raise RuntimeError("Invalid payload: missing timestamp/date column")
+
+    if key not in df.columns:
+        raise RuntimeError(f"Invalid payload: missing required column '{key}'")
+
+    out = df[[key]].sort_index()
+    start_ts = pd.Timestamp(start)
+    end_ts = pd.Timestamp(end)
+    start_ts = start_ts.tz_localize("UTC") if start_ts.tzinfo is None else start_ts.tz_convert("UTC")
+    end_ts = end_ts.tz_localize("UTC") if end_ts.tzinfo is None else end_ts.tz_convert("UTC")
+    clipped = out[(out.index >= start_ts) & (out.index <= end_ts)]
+    if clipped.empty:
+        raise RuntimeError("Invalid payload: no records in requested window")
+    return clipped
@@ -0,0 +1,155 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+import pandas as pd
+
+from fx_quant_engine.ingestion.base import BaseDataAdapter
+from fx_quant_engine.schemas import DataFetchResult, SourceUsageRecord
+from fx_quant_engine.utils.logging import get_logger
+
+
+class DataSourceRouter:
+    def __init__(
+        self,
+        adapters: dict[str, BaseDataAdapter],
+        config: dict[str, Any],
+    ) -> None:
+        self.adapters = adapters
+        self.config = config
+        self.logger = get_logger(self.__class__.__name__)
+        self.usage_log: list[SourceUsageRecord] = []
+
+        cb_cfg = config.get("circuit_breaker", {})
+        self.failure_threshold = int(cb_cfg.get("failure_threshold", 3))
+        self.cooldown_seconds = int(cb_cfg.get("cooldown_seconds", 120))
+        self.source_state: dict[str, dict[str, Any]] = {
+            name: {"failures": 0, "open_until": None} for name in adapters
+        }
+
+    def _get_candidate_sources(self, asset: str) -> list[str]:
+        source_cfg = self.config
+        mapped = source_cfg.get("asset_source_map", {}).get(asset)
+        if mapped:
+            return mapped
+        g10 = asset in {"EURUSD", "GBPUSD", "USDJPY", "AUDUSD", "USDCAD", "USDCHF"}
+        key = "g10" if g10 else "default"
+        return source_cfg.get("source_priority", {}).get(key, ["mock"])
+
+    def _is_enabled(self, source: str) -> bool:
+        return bool(self.config.get("enabled_sources", {}).get(source, False))
+
+    def _tag(self, source: str) -> tuple[str, int]:
+        reliability = self.config.get("reliability_tags", {}).get(source, "low")
+        latency_ms = int(self.config.get("latency_tags_ms", {}).get(source, 1000))
+        return reliability, latency_ms
+
+    def _source_available(self, source: str, now: datetime) -> bool:
+        state = self.source_state.get(source, {"open_until": None})
+        open_until = state.get("open_until")
+        if open_until is None:
+            return True
+        if now >= open_until:
+            state["open_until"] = None
+            state["failures"] = 0
+            self.source_state[source] = state
+            return True
+        return False
+
+    def _register_success(self, source: str) -> None:
+        state = self.source_state.get(source, {"failures": 0, "open_until": None})
+        state["failures"] = 0
+        state["open_until"] = None
+        self.source_state[source] = state
+
+    def _register_failure(self, source: str, now: datetime) -> None:
+        state = self.source_state.get(source, {"failures": 0, "open_until": None})
+        state["failures"] = int(state.get("failures", 0)) + 1
+        if state["failures"] >= self.failure_threshold:
+            state["open_until"] = now + timedelta(seconds=self.cooldown_seconds)
+            self.logger.warning(
+                "Opening circuit for source=%s until %s after %s failures",
+                source,
+                state["open_until"].isoformat(),
+                state["failures"],
+            )
+        self.source_state[source] = state
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("price", asset, start, end)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("macro", key, start, end)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("rate", asset, start, end)
+
+    def _fetch_with_fallback(
+        self,
+        usage_type: str,
+        key: str,
+        start: datetime,
+        end: datetime,
+    ) -> DataFetchResult:
+        candidates = self._get_candidate_sources(key)
+        last_err = "No source attempted"
+        now = datetime.now(timezone.utc)
+
+        for source in candidates:
+            if not self._is_enabled(source):
+                continue
+            if not self._source_available(source, now):
+                self.logger.info("Skipping source=%s due to cooldown window", source)
+                continue
+
+            adapter = self.adapters.get(source)
+            if adapter is None:
+                continue
+
+            try:
+                if usage_type == "price":
+                    data = adapter.fetch_price_data(key, start, end)
+                elif usage_type == "macro":
+                    data = adapter.fetch_macro_data(key, start, end)
+                else:
+                    data = adapter.fetch_rate_data(key, start, end)
+
+                self._register_success(source)
+                reliability, latency_ms = self._tag(source)
+                self.usage_log.append(SourceUsageRecord(asset=key, source=source, usage_type=usage_type))
+                self.logger.info("Fetched %s data for %s via %s", usage_type, key, source)
+                return DataFetchResult(
+                    data=data,
+                    source=source,
+                    reliability=reliability,
+                    latency_ms=latency_ms,
+                )
+            except Exception as exc:  # noqa: BLE001
+                last_err = str(exc)
+                self._register_failure(source, now)
+                self.usage_log.append(
+                    SourceUsageRecord(
+                        asset=key,
+                        source=source,
+                        usage_type=usage_type,
+                        success=False,
+                        error=last_err,
+                    )
+                )
+                self.logger.warning(
+                    "Failed %s data fetch for %s via %s: %s",
+                    usage_type,
+                    key,
+                    source,
+                    last_err,
+                )
+
+        return DataFetchResult(
+            data=pd.DataFrame(),
+            source="none",
+            reliability="low",
+            latency_ms=9999,
+            success=False,
+            error=last_err,
+        )
@@ -0,0 +1,3 @@
+from fx_quant_engine.integration.hooks import IntegrationHooks
+
+__all__ = ["IntegrationHooks"]
@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from fx_quant_engine.schemas import RegimeState
+
+
+class IntegrationHooks:
+    def export_scores(self, features: pd.DataFrame, regime: RegimeState) -> dict[str, float]:
+        if features.empty:
+            return {
+                "usd_strength_score": 0.0,
+                "inr_stress_score": 0.0,
+                "carry_attractiveness": 0.0,
+                "fx_volatility_stress": 0.0,
+                "risk_on_off_score": 0.0,
+            }
+        row = features.iloc[-1]
+        return {
+            "usd_strength_score": float(row.get("usd_strength_score", 0.0)),
+            "inr_stress_score": float(row.get("em_stress_proxy", 0.0) + row.get("inr_vol_clustering", 0.0)),
+            "carry_attractiveness": float(row.get("carry_proxy", 0.0)),
+            "fx_volatility_stress": float(row.get("realized_vol", 0.0)),
+            "risk_on_off_score": float(row.get("risk_on_off_proxy", 0.0)),
+            "regime_stability": float(regime.stability),
+        }
@@ -0,0 +1,4 @@
+from fx_quant_engine.models.ensemble import EnsembleModel
+from fx_quant_engine.models.relative_value import RelativeValueModel
+
+__all__ = ["EnsembleModel", "RelativeValueModel"]
@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+class EnsembleModel:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def directional_score(self, row: pd.Series) -> tuple[float, dict[str, float], float]:
+        weights = self.config.get("directional", {}).get("model_weights", {})
+        component = {
+            "trend": float(row.get("trend_strength", 0.0)),
+            "momentum": float(row.get("momentum_multi_horizon", 0.0)),
+            "mean_reversion": -float(row.get("mean_reversion_zscore", 0.0)),
+            "carry": float(row.get("carry_proxy", 0.0)),
+            "macro": float(row.get("risk_on_off_proxy", 0.0)),
+        }
+        wsum = sum(float(weights.get(k, 0.0)) for k in component)
+        score = 0.0
+        if wsum > 0:
+            score = sum(component[k] * float(weights.get(k, 0.0)) for k in component) / wsum
+        normalized = float(np.tanh(score))
+        signs = [np.sign(v) for v in component.values() if abs(v) > 1e-12]
+        agreement = float((abs(sum(signs)) / len(signs))) if signs else 0.0
+        return normalized, component, agreement
+
+    def relative_value_score(self, row_a: pd.Series, row_b: pd.Series) -> tuple[float, dict[str, float], float]:
+        weights = self.config.get("relative_value", {}).get("model_weights", {})
+        spread = float(row_a.get("mean_reversion_zscore", 0.0) - row_b.get("mean_reversion_zscore", 0.0))
+        mom_diff = float(row_a.get("momentum_multi_horizon", 0.0) - row_b.get("momentum_multi_horizon", 0.0))
+        carry_diff = float(row_a.get("carry_proxy", 0.0) - row_b.get("carry_proxy", 0.0))
+        component = {"spread": spread, "momentum_diff": mom_diff, "carry_diff": carry_diff}
+        wsum = sum(float(weights.get(k, 0.0)) for k in component)
+        score = 0.0
+        if wsum > 0:
+            score = sum(component[k] * float(weights.get(k, 0.0)) for k in component) / wsum
+        normalized = float(np.tanh(score))
+        signs = [np.sign(v) for v in component.values() if abs(v) > 1e-12]
+        agreement = float((abs(sum(signs)) / len(signs))) if signs else 0.0
+        return normalized, component, agreement
@@ -0,0 +1,15 @@
+"""Placeholder for future model extensions.
+
+This module is intentionally lightweight in the starter phase. Future additions can
+include gradient boosting, probabilistic forecasts, and online learning modules.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass
+class ModelExtensionConfig:
+    enabled: bool = False
+    name: str = "baseline_extension"
@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass
+class RelativeValueResult:
+    score: float
+    hedge_ratio: float
+    spread_zscore: float
+    expected_volatility: float
+    drivers: dict[str, float]
+    long_asset: str
+    short_asset: str
+
+
+class RelativeValueModel:
+    def __init__(self, lookback: int = 60) -> None:
+        self.lookback = lookback
+
+    def generate(
+        self,
+        asset_a: str,
+        asset_b: str,
+        prices_a: pd.Series,
+        prices_b: pd.Series,
+        features_a: pd.DataFrame,
+        features_b: pd.DataFrame,
+    ) -> RelativeValueResult:
+        pa, pb = self._align(prices_a, prices_b)
+        la = pa.astype(float).apply(np.log)
+        lb = pb.astype(float).apply(np.log)
+
+        hedge_ratio = self._hedge_ratio(la.tail(self.lookback), lb.tail(self.lookback))
+        spread = la - hedge_ratio * lb
+
+        window = max(20, self.lookback // 2)
+        zscore = self._zscore(spread, window=window)
+        spread_z = float(zscore.iloc[-1]) if len(zscore) else 0.0
+
+        row_a = features_a.iloc[-1]
+        row_b = features_b.iloc[-1]
+        momentum_diff = float(row_a.get("momentum_multi_horizon", 0.0) - row_b.get("momentum_multi_horizon", 0.0))
+        carry_diff = float(row_a.get("carry_proxy", 0.0) - row_b.get("carry_proxy", 0.0))
+
+        raw = -0.60 * spread_z + 0.25 * momentum_diff + 0.15 * carry_diff
+        score = float(np.tanh(raw))
+
+        if score >= 0:
+            long_asset, short_asset = asset_a, asset_b
+        else:
+            long_asset, short_asset = asset_b, asset_a
+
+        expected_vol = float(spread.diff().tail(window).std() * np.sqrt(252.0)) if len(spread) > 2 else 0.0
+        drivers = {
+            "spread_zscore": spread_z,
+            "momentum_diff": momentum_diff,
+            "carry_diff": carry_diff,
+            "hedge_ratio": float(hedge_ratio),
+        }
+        return RelativeValueResult(
+            score=abs(score),
+            hedge_ratio=float(hedge_ratio),
+            spread_zscore=spread_z,
+            expected_volatility=max(expected_vol, 0.0),
+            drivers=drivers,
+            long_asset=long_asset,
+            short_asset=short_asset,
+        )
+
+    @staticmethod
+    def _align(a: pd.Series, b: pd.Series) -> tuple[pd.Series, pd.Series]:
+        joined = pd.concat([a.rename("a"), b.rename("b")], axis=1).dropna()
+        return joined["a"], joined["b"]
+
+    @staticmethod
+    def _hedge_ratio(a: pd.Series, b: pd.Series) -> float:
+        denom = float(np.var(b))
+        if abs(denom) < 1e-12:
+            return 1.0
+        return float(np.cov(a, b)[0, 1] / denom)
+
+    @staticmethod
+    def _zscore(series: pd.Series, window: int) -> pd.Series:
+        mu = series.rolling(window).mean()
+        sigma = series.rolling(window).std()
+        return ((series - mu) / (sigma + 1e-9)).fillna(0.0)
@@ -0,0 +1,3 @@
+from fx_quant_engine.outputs.formatter import OutputFormatter
+
+__all__ = ["OutputFormatter"]
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from typing import Any
+
+from fx_quant_engine.schemas import SignalPayload
+
+
+class OutputFormatter:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config.get("output", {})
+
+    def to_json_payload(self, signal: SignalPayload) -> dict[str, Any]:
+        p = self.config.get("precision", 4)
+        data = signal.to_dict()
+        data["signal_strength"] = round(float(data["signal_strength"]), p)
+        data["confidence"] = round(float(data["confidence"]), p)
+        data["expected_volatility"] = round(float(data["expected_volatility"]), p)
+        data["position_sizing_multiplier"] = round(float(data["position_sizing_multiplier"]), p)
+        return data
+
+    def trader_summary(self, signal: SignalPayload) -> str:
+        return (
+            f"{signal.asset} | {signal.signal_type} | {signal.signal_direction} | "
+            f"strength={signal.signal_strength:.2f} confidence={signal.confidence:.2f} "
+            f"action={signal.recommended_action} size={signal.position_sizing_multiplier:.2f} "
+            f"regime={signal.regime.get('combined', 'neutral')}"
+        )
+
+    def dashboard_payload(self, signal: SignalPayload) -> dict[str, Any]:
+        return {
+            "asset": signal.asset,
+            "score": signal.signal_strength,
+            "confidence": signal.confidence,
+            "action": signal.recommended_action,
+            "risk_flags": signal.risk_flags,
+            "regime": signal.regime,
+        }
@@ -0,0 +1,3 @@
+from fx_quant_engine.preprocessing.cleaning import preprocess_market_data
+
+__all__ = ["preprocess_market_data"]
@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+import pandas as pd
+
+
+def preprocess_market_data(df: pd.DataFrame) -> pd.DataFrame:
+    if df.empty:
+        return df
+    out = df.copy().sort_index()
+    out = out[~out.index.duplicated(keep="last")]
+    out = out.ffill().dropna(how="all")
+    return out
@@ -0,0 +1,3 @@
+from fx_quant_engine.regime.engine import RegimeEngine
+
+__all__ = ["RegimeEngine"]
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.schemas import RegimeState
+
+
+class RegimeEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def detect(self, features: pd.DataFrame) -> RegimeState:
+        if features.empty:
+            return RegimeState("neutral", "normal", "neutral", "risk_on", "neutral", 0.0)
+
+        row = features.iloc[-1]
+        trend = self._trend_regime(row)
+        vol = self._vol_regime(features)
+        dollar = self._dollar_regime(row)
+        risk = self._risk_regime(row, vol)
+        combined = self._combine(trend, vol, dollar, risk)
+        stability = self._stability(features)
+        return RegimeState(trend, vol, dollar, risk, combined, stability)
+
+    def _trend_regime(self, row: pd.Series) -> str:
+        score = float(row.get("trend_strength", 0.0))
+        th = float(self.config.get("trend", {}).get("slope_threshold", 0.0005))
+        band = float(self.config.get("trend", {}).get("mr_band", 0.003))
+        if score > th:
+            return "trend"
+        if score < -band:
+            return "mean_reversion"
+        return "neutral"
+
+    def _vol_regime(self, features: pd.DataFrame) -> str:
+        vol = features.get("realized_vol", pd.Series(0.0, index=features.index)).fillna(0.0)
+        cur = float(vol.iloc[-1])
+        ql = float(vol.quantile(self.config.get("volatility", {}).get("low_quantile", 0.3)))
+        qh = float(vol.quantile(self.config.get("volatility", {}).get("high_quantile", 0.7)))
+        qs = float(vol.quantile(self.config.get("volatility", {}).get("stress_quantile", 0.9)))
+        if cur >= qs:
+            return "stress"
+        if cur >= qh:
+            return "high"
+        if cur <= ql:
+            return "low"
+        return "normal"
+
+    def _dollar_regime(self, row: pd.Series) -> str:
+        usd = float(row.get("usd_strength_score", 0.0))
+        band = float(self.config.get("dollar", {}).get("neutral_band", 0.2))
+        if usd > band:
+            return "strengthening"
+        if usd < -band:
+            return "weakening"
+        return "neutral"
+
+    def _risk_regime(self, row: pd.Series, vol_regime: str) -> str:
+        risk_score = float(row.get("risk_on_off_proxy", 0.0))
+        threshold = float(self.config.get("risk", {}).get("risk_off_equity_threshold", -0.01))
+        if vol_regime == "stress":
+            return "stressed"
+        if risk_score < threshold:
+            return "risk_off"
+        return "risk_on"
+
+    def _combine(self, trend: str, vol: str, dollar: str, risk: str) -> str:
+        if vol == "stress" or risk == "stressed":
+            return "defensive"
+        if trend == "trend" and risk == "risk_on":
+            return "pro_trend"
+        if trend == "mean_reversion" and risk != "stressed":
+            return "mean_revert"
+        if dollar == "strengthening":
+            return "usd_dominant"
+        return "neutral"
+
+    def _stability(self, features: pd.DataFrame) -> float:
+        cols = [c for c in ["trend_strength", "realized_vol", "usd_strength_score", "risk_on_off_proxy"] if c in features]
+        if not cols:
+            return 0.0
+        tail = features[cols].tail(10)
+        vol = float(tail.std().mean())
+        return float(np.clip(1.0 - vol, 0.0, 1.0))
@@ -0,0 +1,3 @@
+from fx_quant_engine.risk.engine import RiskEngine
+
+__all__ = ["RiskEngine"]
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+from fx_quant_engine.schemas import RegimeState, SignalPayload
+from fx_quant_engine.utils.time import is_within_market_hours
+
+
+class RiskEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config.get("risk", {})
+
+    def apply(self, signal: SignalPayload, regime: RegimeState, reliability: str, liquidity_ok: bool, event_risk: float) -> SignalPayload:
+        penalties = self.config.get("data_reliability_penalty", {})
+        rel_mult = float(penalties.get(reliability, 0.6))
+        size = signal.signal_strength * signal.confidence * rel_mult
+
+        if regime.volatility in {"high", "stress"}:
+            size *= float(self.config.get("stressed_regime_multiplier", 0.5))
+            signal.risk_flags.append("high_volatility_regime")
+
+        if not liquidity_ok:
+            size *= float(self.config.get("illiquid_market_multiplier", 0.6))
+            signal.risk_flags.append("liquidity_constraint")
+
+        if event_risk > 0.6:
+            size *= 0.5
+            signal.risk_flags.append("event_risk_high")
+
+        if not self._market_open_now():
+            signal.risk_flags.append("outside_primary_market_hours")
+            size *= 0.8
+
+        signal.position_sizing_multiplier = max(0.0, min(size, 1.0))
+        signal.recommended_action = self._action(signal.position_sizing_multiplier)
+        return signal
+
+    def _market_open_now(self) -> bool:
+        now = datetime.now(timezone.utc)
+        open_utc = self.config.get("market_hours", {}).get("inr_derivatives_open_utc", "03:45")
+        close_utc = self.config.get("market_hours", {}).get("inr_derivatives_close_utc", "10:00")
+        return is_within_market_hours(now, open_utc, close_utc)
+
+    @staticmethod
+    def _action(size: float) -> str:
+        if size >= 0.75:
+            return "enter"
+        if size >= 0.45:
+            return "enter_small"
+        if size >= 0.25:
+            return "hold"
+        if size > 0.0:
+            return "reduce"
+        return "no_trade"
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+from typing import Any
+
+import pandas as pd
+
+
+@dataclass
+class DataFetchResult:
+    data: pd.DataFrame
+    source: str
+    reliability: str
+    latency_ms: int
+    success: bool = True
+    error: str | None = None
+
+
+@dataclass
+class RegimeState:
+    trend: str
+    volatility: str
+    dollar: str
+    risk: str
+    combined: str
+    stability: float
+
+
+@dataclass
+class SignalPayload:
+    engine: str
+    timestamp: str
+    asset: str
+    signal_type: str
+    signal_direction: str
+    signal_strength: float
+    confidence: float
+    regime: dict[str, Any]
+    expected_volatility: float
+    risk_flags: list[str]
+    drivers: dict[str, Any]
+    recommended_action: str
+    position_sizing_multiplier: float
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class EvaluationReport:
+    generated_at: str
+    hit_rate: float
+    avg_forward_return: float
+    regime_performance: dict[str, float]
+    signal_decay: dict[str, float]
+    feature_importance: dict[str, float]
+    drift_flags: list[str]
+    calibration_score: float
+
+
+@dataclass
+class BacktestResult:
+    pair_level_returns: dict[str, float]
+    portfolio_return: float
+    max_drawdown: float
+    turnover: float
+    metrics: dict[str, float]
+
+
+@dataclass
+class SourceUsageRecord:
+    asset: str
+    source: str
+    usage_type: str
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    success: bool = True
+    error: str | None = None
@@ -0,0 +1,4 @@
+from fx_quant_engine.signals.confidence import ConfidenceEngine
+from fx_quant_engine.signals.engine import SignalEngine
+
+__all__ = ["SignalEngine", "ConfidenceEngine"]
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+
+class ConfidenceEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def compute(
+        self,
+        feature_agreement: float,
+        model_agreement: float,
+        regime_stability: float,
+        data_quality: float,
+        volatility: float,
+        event_risk: float,
+    ) -> float:
+        c = self.config.get("confidence", {})
+        agreement_w = float(c.get("agreement_weight", 0.25))
+        regime_w = float(c.get("regime_stability_weight", 0.20))
+        data_w = float(c.get("data_quality_weight", 0.20))
+        vol_w = float(c.get("volatility_penalty_weight", 0.20))
+        event_w = float(c.get("event_penalty_weight", 0.15))
+
+        agreement = 0.5 * (feature_agreement + model_agreement)
+        vol_penalty = np.clip(volatility, 0.0, 1.0)
+        event_penalty = np.clip(event_risk, 0.0, 1.0)
+
+        raw = (
+            agreement_w * agreement
+            + regime_w * np.clip(regime_stability, 0.0, 1.0)
+            + data_w * np.clip(data_quality, 0.0, 1.0)
+            - vol_w * vol_penalty
+            - event_w * event_penalty
+        )
+        return float(np.clip((raw + 1.0) / 2.0, 0.0, 1.0))
@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from fx_quant_engine.schemas import RegimeState, SignalPayload
+
+
+class SignalEngine:
+    def __init__(self, engine_name: str = "fx_quant_engine") -> None:
+        self.engine_name = engine_name
+
+    def directional_signal(
+        self,
+        asset: str,
+        feature_row: pd.Series,
+        regime: RegimeState,
+        score: float,
+        confidence: float,
+        drivers: dict[str, float],
+        expected_volatility: float,
+    ) -> SignalPayload:
+        direction = self._direction(asset, score)
+        return SignalPayload(
+            engine=self.engine_name,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+            asset=asset,
+            signal_type="directional",
+            signal_direction=direction,
+            signal_strength=float(np.clip(abs(score), 0.0, 1.0)),
+            confidence=float(np.clip(confidence, 0.0, 1.0)),
+            regime=regime.__dict__,
+            expected_volatility=float(max(0.0, expected_volatility)),
+            risk_flags=[],
+            drivers={k: float(v) for k, v in drivers.items()},
+            recommended_action="hold",
+            position_sizing_multiplier=0.0,
+        )
+
+    def relative_value_signal(
+        self,
+        long_asset: str,
+        short_asset: str,
+        regime: RegimeState,
+        score: float,
+        confidence: float,
+        drivers: dict[str, float],
+        expected_volatility: float,
+    ) -> SignalPayload:
+        direction = f"long_{long_asset}_vs_{short_asset}"
+        asset = f"{long_asset}:{short_asset}"
+        return SignalPayload(
+            engine=self.engine_name,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+            asset=asset,
+            signal_type="relative_value",
+            signal_direction=direction,
+            signal_strength=float(np.clip(abs(score), 0.0, 1.0)),
+            confidence=float(np.clip(confidence, 0.0, 1.0)),
+            regime=regime.__dict__,
+            expected_volatility=float(max(0.0, expected_volatility)),
+            risk_flags=[],
+            drivers={k: float(v) for k, v in drivers.items()},
+            recommended_action="hold",
+            position_sizing_multiplier=0.0,
+        )
+
+    def _direction(self, asset: str, score: float) -> str:
+        if asset.startswith("USD"):
+            return "long_usd" if score >= 0 else "short_usd"
+        return "long_base" if score >= 0 else "short_base"
@@ -0,0 +1,4 @@
+from fx_quant_engine.utils.config import load_all_configs, load_yaml
+from fx_quant_engine.utils.logging import get_logger
+
+__all__ = ["load_yaml", "load_all_configs", "get_logger"]
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+def load_yaml(path: str | Path) -> dict[str, Any]:
+    with Path(path).open("r", encoding="utf-8") as f:
+        data = yaml.safe_load(f) or {}
+    if not isinstance(data, dict):
+        raise ValueError(f"Config file must contain a mapping: {path}")
+    return data
+
+
+def load_all_configs(config_dir: str | Path) -> dict[str, dict[str, Any]]:
+    cfg_dir = Path(config_dir)
+    return {
+        "universe": load_yaml(cfg_dir / "universe.yaml"),
+        "data_sources": load_yaml(cfg_dir / "data_sources.yaml"),
+        "features": load_yaml(cfg_dir / "features.yaml"),
+        "regimes": load_yaml(cfg_dir / "regimes.yaml"),
+        "models": load_yaml(cfg_dir / "models.yaml"),
+        "risk": load_yaml(cfg_dir / "risk.yaml"),
+        "output": load_yaml(cfg_dir / "output.yaml"),
+    }
@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+import logging
+
+
+def get_logger(name: str) -> logging.Logger:
+    logger = logging.getLogger(name)
+    if not logger.handlers:
+        handler = logging.StreamHandler()
+        fmt = "%(asctime)s | %(name)s | %(levelname)s | %(message)s"
+        handler.setFormatter(logging.Formatter(fmt))
+        logger.addHandler(handler)
+        logger.setLevel(logging.INFO)
+    return logger
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from datetime import datetime, time, timezone
+
+
+def is_within_market_hours(
+    now_utc: datetime,
+    open_hhmm: str,
+    close_hhmm: str,
+) -> bool:
+    open_h, open_m = [int(x) for x in open_hhmm.split(":")]
+    close_h, close_m = [int(x) for x in close_hhmm.split(":")]
+    open_t = time(hour=open_h, minute=open_m)
+    close_t = time(hour=close_h, minute=close_m)
+    t = now_utc.astimezone(timezone.utc).time()
+    return open_t <= t <= close_t
@@ -0,0 +1,47 @@
+name: options-quant-engine-ci
+
+on:
+  push:
+    paths:
+      - "options_quant_engine/**"
+  pull_request:
+    paths:
+      - "options_quant_engine/**"
+  workflow_dispatch:
+
+jobs:
+  lint-test-build:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: options_quant_engine
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -e .[dev]
+
+      - name: Lint
+        run: ruff check .
+
+      - name: Test
+        run: pytest
+
+      - name: Generate sample run artifact
+        run: python scripts/run_engine.py
+
+      - name: Upload run artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: options-engine-runs
+          path: options_quant_engine/examples/runs/*.json
+          if-no-files-found: warn
@@ -0,0 +1,193 @@
+# options_quant_engine
+
+A professional, modular, explainable, and production-ready options quant engine with a clean, extensible architecture for standalone deployment.
+
+## What This Engine Does
+
+- Ingests market/macro/rate data from multiple pluggable adapters
+- Uses configurable source priority and fallback logic per asset
+- Engineers economically meaningful features through a registry-based pipeline
+- Detects interpretable market regimes
+- Generates directional and relative-value signals
+- Separates signal strength from confidence
+- Applies risk overlays and outputs action recommendations
+- Supports evaluation and realistic backtest scaffolding
+- Exports integration hooks for cross-engine multi-asset platforms
+
+## Underlying Universe
+
+Primary INR pairs:
+
+- USDINR
+- EURINR
+- GBPINR
+- JPYINR
+
+Secondary G10 pairs:
+
+- EURUSD
+- GBPUSD
+- USDJPY
+- AUDUSD
+- USDCAD
+- USDCHF
+
+## Repository Structure
+
+- options_quant_engine/ingestion: adapters + fallback router
+- options_quant_engine/preprocessing: data cleaning/alignment
+- options_quant_engine/features: registry + modular feature pipeline
+- options_quant_engine/regime: explainable regime detection
+- options_quant_engine/models: model scoring and extension points
+- options_quant_engine/signals: directional/RV signal + confidence engine
+- options_quant_engine/risk: risk overlays and action mapping
+- options_quant_engine/evaluation: signal quality diagnostics
+- options_quant_engine/backtest: no-lookahead pair/portfolio simulation
+- options_quant_engine/outputs: machine-readable + trader-readable payloads
+- options_quant_engine/integration: cross-engine score exports
+- config: YAML-driven behavior
+- scripts: runnable examples
+- tests: unit tests
+- examples: sample payloads and run artifacts
+
+## Data Adapter Architecture
+
+Base interface:
+
+```python
+class BaseDataAdapter:
+    def fetch_price_data(self, asset, start, end):
+        pass
+
+    def fetch_macro_data(self, key, start, end):
+        pass
+
+    def fetch_rate_data(self, asset, start, end):
+        pass
+
+    def health_check(self):
+        pass
+```
+
+Included adapters:
+
+- BreezeAdapter (ICICI Breeze)
+- ZerodhaAdapter (optional via config)
+- NSEAdapter
+- RBIAdapter
+- FreeFXAdapter
+- MockAdapter
+
+Live API stubs are environment-driven and include strict timeout/retry logic:
+
+- OQE_HTTP_TIMEOUT_SEC
+- OQE_HTTP_MAX_ATTEMPTS
+- OQE_HTTP_BACKOFF_SEC
+- BREEZE_LIVE_ENABLED, BREEZE_BASE_URL, BREEZE_API_KEY, BREEZE_API_SECRET
+- ZERODHA_LIVE_ENABLED, ZERODHA_BASE_URL, ZERODHA_API_KEY, ZERODHA_ACCESS_TOKEN
+- NSE_LIVE_ENABLED, NSE_BASE_URL
+- RBI_LIVE_ENABLED, RBI_BASE_URL
+
+## Config-Driven Files
+
+- config/universe.yaml
+- config/data_sources.yaml
+- config/features.yaml
+- config/regimes.yaml
+- config/models.yaml
+- config/risk.yaml
+- config/output.yaml
+
+## Quickstart
+
+1. Create and activate a Python 3.10+ environment.
+1. Install package and dev dependencies:
+
+```bash
+pip install -e .[dev]
+```
+
+1. Run tests:
+
+```bash
+pytest
+```
+
+1. Run engine example:
+
+```bash
+python scripts/run_engine.py
+```
+
+The script saves artifacts to examples/runs.
+
+## Signal Output Schema
+
+Each signal includes:
+
+- direction
+- signal strength
+- confidence (separate module)
+- regime context
+- expected volatility
+- key drivers
+- risk flags
+- recommended action
+- position sizing multiplier
+
+See examples/sample_signal.json.
+
+## Relative Value Spread Model
+
+Relative-value signal generation uses a true spread framework:
+
+- explicit hedge ratio estimation from aligned log prices
+- spread z-score signal component
+- pair-level momentum and carry differentials
+- expected spread volatility and long/short leg assignment
+
+The output includes `hedge_ratio` and `spread_zscore` in RV payloads.
+
+## CI
+
+GitHub Actions workflow: `.github/workflows/ci.yml`
+
+- ruff lint checks
+- pytest execution
+- sample run generation
+- artifact upload of `examples/runs/*.json`
+
+## Build Phases Coverage
+
+Phase 1:
+
+- repo structure
+- config system
+- ingestion + preprocessing
+- basic features
+
+Phase 2:
+
+- regime detection
+- signal engine
+- confidence engine
+- outputs
+
+Phase 3:
+
+- risk layer
+- evaluation framework
+- backtesting scaffolding
+
+Phase 4 starter:
+
+- model extension hooks
+- integration hooks
+- tests
+- docs
+
+## Notes
+
+- No credentials are hardcoded.
+- Live adapter wiring is stubbed with production-style retries/timeouts and env credentials.
+- Mock pathways remain default for deterministic testing and offline development.
@@ -0,0 +1,50 @@
+enabled_sources:
+  breeze: true
+  zerodha: false
+  nse: true
+  rbi: true
+  freefx: true
+  mock: true
+
+source_priority:
+  default: [nse, rbi, freefx, mock]
+  g10: [freefx, mock]
+
+asset_source_map:
+  USDINR: [nse, breeze, rbi, mock]
+  EURINR: [nse, breeze, freefx, mock]
+  GBPINR: [nse, freefx, mock]
+  JPYINR: [nse, freefx, mock]
+  EURUSD: [freefx, mock]
+  GBPUSD: [freefx, mock]
+  USDJPY: [freefx, mock]
+  AUDUSD: [freefx, mock]
+  USDCAD: [freefx, mock]
+  USDCHF: [freefx, mock]
+
+reliability_tags:
+  breeze: medium
+  zerodha: medium
+  nse: high
+  rbi: high
+  freefx: medium
+  mock: low
+
+latency_tags_ms:
+  breeze: 350
+  zerodha: 250
+  nse: 500
+  rbi: 800
+  freefx: 700
+  mock: 1
+
+live_api:
+  breeze_live_enabled: false
+  zerodha_live_enabled: false
+  nse_live_enabled: false
+  rbi_live_enabled: false
+
+http_controls:
+  timeout_sec: 5
+  max_attempts: 3
+  backoff_sec: 0.5
@@ -0,0 +1,28 @@
+features:
+  returns: true
+  moving_averages: true
+  breakouts: true
+  trend_strength: true
+  momentum_multi_horizon: true
+  momentum_persistence: true
+  momentum_acceleration: true
+  mean_reversion_zscore: true
+  bollinger_position: true
+  realized_vol: true
+  atr_proxy: true
+  vol_regime_proxy: true
+  carry_proxy: true
+  usd_strength_score: true
+  risk_on_off_proxy: true
+  inr_crude_proxy: true
+  em_stress_proxy: true
+  inr_vol_clustering: true
+  rbi_intervention_proxy: true
+  cross_asset_bond_proxy: true
+  cross_asset_commodity_proxy: true
+  cross_asset_equity_proxy: true
+
+windows:
+  short: 5
+  medium: 20
+  long: 60
@@ -0,0 +1,22 @@
+directional:
+  model_weights:
+    trend: 0.35
+    momentum: 0.25
+    mean_reversion: 0.15
+    carry: 0.10
+    macro: 0.15
+
+relative_value:
+  lookback: 60
+  spread_zscore_threshold: 1.0
+  model_weights:
+    spread: 0.5
+    momentum_diff: 0.3
+    carry_diff: 0.2
+
+confidence:
+  agreement_weight: 0.25
+  regime_stability_weight: 0.20
+  data_quality_weight: 0.20
+  volatility_penalty_weight: 0.20
+  event_penalty_weight: 0.15
@@ -0,0 +1,5 @@
+output:
+  include_dashboard_payload: true
+  include_trader_summary: true
+  precision: 4
+  engine_name: options_quant_engine
@@ -0,0 +1,15 @@
+trend:
+  slope_threshold: 0.0005
+  mr_band: 0.003
+
+volatility:
+  low_quantile: 0.3
+  high_quantile: 0.7
+  stress_quantile: 0.9
+
+dollar:
+  neutral_band: 0.2
+
+risk:
+  stress_vol_threshold: 0.75
+  risk_off_equity_threshold: -0.01
@@ -0,0 +1,13 @@
+risk:
+  target_vol: 0.10
+  max_gross_exposure: 1.0
+  max_single_asset_exposure: 0.25
+  stressed_regime_multiplier: 0.5
+  illiquid_market_multiplier: 0.6
+  data_reliability_penalty:
+    high: 1.0
+    medium: 0.85
+    low: 0.6
+  market_hours:
+    inr_derivatives_open_utc: "03:45"
+    inr_derivatives_close_utc: "10:00"
@@ -0,0 +1,12 @@
+primary_inr_pairs:
+  - USDINR
+  - EURINR
+  - GBPINR
+  - JPYINR
+secondary_g10_pairs:
+  - EURUSD
+  - GBPUSD
+  - USDJPY
+  - AUDUSD
+  - USDCAD
+  - USDCHF
@@ -0,0 +1,28 @@
+{
+  "engine": "options_quant_engine",
+  "timestamp": "2026-03-30T10:00:00",
+  "asset": "USDINR",
+  "signal_type": "directional",
+  "signal_direction": "long_usd",
+  "signal_strength": 0.74,
+  "confidence": 0.68,
+  "regime": {
+    "trend": "trend",
+    "volatility": "normal",
+    "dollar": "strengthening",
+    "risk": "risk_off",
+    "combined": "usd_dominant",
+    "stability": 0.71
+  },
+  "expected_volatility": 0.089,
+  "risk_flags": ["outside_primary_market_hours"],
+  "drivers": {
+    "trend": 0.51,
+    "momentum": 0.36,
+    "mean_reversion": -0.12,
+    "carry": 0.09,
+    "macro": -0.07
+  },
+  "recommended_action": "enter_small",
+  "position_sizing_multiplier": 0.65
+}
@@ -0,0 +1,5 @@
+"""Options Quant Engine package."""
+
+from options_quant_engine.engine import OptionsQuantEngine
+
+__all__ = ["OptionsQuantEngine"]
@@ -0,0 +1,3 @@
+from options_quant_engine.backtest.engine import BacktestEngine
+
+__all__ = ["BacktestEngine"]
@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.schemas import BacktestResult
+
+
+class BacktestEngine:
+    def __init__(self, transaction_cost_bps: float = 1.5, slippage_bps: float = 1.0) -> None:
+        self.tc = transaction_cost_bps / 10000.0
+        self.slippage = slippage_bps / 10000.0
+
+    def simulate_pair(self, prices: pd.Series, signal_strength: pd.Series) -> pd.Series:
+        returns = prices.pct_change().fillna(0.0)
+        position = signal_strength.shift(1).fillna(0.0)  # no lookahead
+        traded = position.diff().abs().fillna(0.0)
+        net = position * returns - traded * (self.tc + self.slippage)
+        return net
+
+    def simulate_portfolio(self, pair_returns: dict[str, pd.Series]) -> BacktestResult:
+        if not pair_returns:
+            return BacktestResult({}, 0.0, 0.0, 0.0, {})
+
+        df = pd.DataFrame(pair_returns).fillna(0.0)
+        port = df.mean(axis=1)
+        equity = (1.0 + port).cumprod()
+        dd = equity / equity.cummax() - 1.0
+
+        pair_level = {k: float(v.mean() * 252.0) for k, v in pair_returns.items()}
+        metrics = {
+            "annualized_return": float(port.mean() * 252.0),
+            "annualized_vol": float(port.std() * np.sqrt(252.0)),
+            "sharpe": float((port.mean() / (port.std() + 1e-9)) * np.sqrt(252.0)),
+        }
+
+        return BacktestResult(
+            pair_level_returns=pair_level,
+            portfolio_return=float((equity.iloc[-1] - 1.0) if len(equity) else 0.0),
+            max_drawdown=float(dd.min() if len(dd) else 0.0),
+            turnover=float(df.diff().abs().sum().sum()),
+            metrics=metrics,
+        )
+
+    def walk_forward(self, prices: pd.Series, signal_strength: pd.Series, train_window: int = 120, test_window: int = 20) -> pd.Series:
+        out = []
+        idx = prices.index
+        i = train_window
+        while i < len(idx):
+            end = min(i + test_window, len(idx))
+            segment = self.simulate_pair(prices.iloc[:end], signal_strength.iloc[:end])
+            out.append(segment.iloc[i:end])
+            i += test_window
+        return pd.concat(out).sort_index() if out else pd.Series(dtype=float)
@@ -0,0 +1,210 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.features.pipeline import FeaturePipeline
+from options_quant_engine.ingestion.adapters import (
+    BreezeAdapter,
+    FreeFXAdapter,
+    MockAdapter,
+    NSEAdapter,
+    RBIAdapter,
+    ZerodhaAdapter,
+)
+from options_quant_engine.ingestion.router import DataSourceRouter
+from options_quant_engine.integration.hooks import IntegrationHooks
+from options_quant_engine.models.ensemble import EnsembleModel
+from options_quant_engine.models.relative_value import RelativeValueModel
+from options_quant_engine.outputs.formatter import OutputFormatter
+from options_quant_engine.preprocessing.cleaning import preprocess_market_data
+from options_quant_engine.regime.engine import RegimeEngine
+from options_quant_engine.risk.engine import RiskEngine
+from options_quant_engine.schemas import SignalPayload
+from options_quant_engine.signals.confidence import ConfidenceEngine
+from options_quant_engine.signals.engine import SignalEngine
+from options_quant_engine.utils.config import load_all_configs
+
+
+class OptionsQuantEngine:
+    def __init__(self, config_dir: str | Path = "config") -> None:
+        self.config = load_all_configs(config_dir)
+        self.adapters = {
+            "breeze": BreezeAdapter(),
+            "zerodha": ZerodhaAdapter(),
+            "nse": NSEAdapter(),
+            "rbi": RBIAdapter(),
+            "freefx": FreeFXAdapter(),
+            "mock": MockAdapter(),
+        }
+        self.router = DataSourceRouter(self.adapters, self.config["data_sources"])
+        self.features = FeaturePipeline(self.config["features"])
+        self.regimes = RegimeEngine(self.config["regimes"])
+        self.models = EnsembleModel(self.config["models"])
+        self.rv_model = RelativeValueModel(lookback=int(self.config["models"].get("relative_value", {}).get("lookback", 60)))
+        self.confidence = ConfidenceEngine(self.config["models"])
+        self.signals = SignalEngine(engine_name=self.config["output"]["output"].get("engine_name", "options_quant_engine"))
+        self.risk = RiskEngine(self.config["risk"])
+        self.output = OutputFormatter(self.config["output"])
+        self.integration = IntegrationHooks()
+
+    def _asset_context(self, asset: str, start: datetime, end: datetime) -> dict[str, Any]:
+        px_res = self.router.fetch_price_data(asset, start, end)
+        macro_res = self.router.fetch_macro_data("macro_proxy", start, end)
+        rate_res = self.router.fetch_rate_data(asset, start, end)
+
+        if not px_res.success or px_res.data.empty:
+            raise RuntimeError(f"No price data available for {asset}: {px_res.error}")
+
+        df = px_res.data.join(macro_res.data, how="left").join(rate_res.data, how="left")
+        if f"{asset}_rate" in df.columns:
+            df["short_rate"] = df[f"{asset}_rate"]
+        df["usd_rate"] = 0.045
+        df["equity_proxy"] = df["close"].pct_change().rolling(5).mean().fillna(0.0)
+        df["crude_proxy"] = (1.0 + df["close"].pct_change().fillna(0.0)).cumprod()
+        df["emfx_proxy"] = df["close"].pct_change().fillna(0.0)
+
+        clean = preprocess_market_data(df)
+        feat = self.features.transform(clean)
+        regime = self.regimes.detect(feat)
+        return {
+            "price": px_res,
+            "macro": macro_res,
+            "rate": rate_res,
+            "clean": clean,
+            "features": feat,
+            "regime": regime,
+        }
+
+    def run_asset(self, asset: str, start: datetime, end: datetime) -> dict[str, Any]:
+        ctx = self._asset_context(asset, start, end)
+        px_res = ctx["price"]
+        feat = ctx["features"]
+        regime = ctx["regime"]
+        row = feat.iloc[-1]
+
+        score, model_drivers, model_agreement = self.models.directional_score(row)
+        feature_agreement = float((row > 0).mean())
+        data_quality = 1.0 if px_res.reliability == "high" else 0.8 if px_res.reliability == "medium" else 0.6
+        volatility = float(min(1.0, row.get("realized_vol", 0.0)))
+        event_risk = float(min(1.0, row.get("rbi_intervention_proxy", 0.0)))
+
+        conf = self.confidence.compute(
+            feature_agreement=feature_agreement,
+            model_agreement=model_agreement,
+            regime_stability=regime.stability,
+            data_quality=data_quality,
+            volatility=volatility,
+            event_risk=event_risk,
+        )
+
+        sig: SignalPayload = self.signals.directional_signal(
+            asset=asset,
+            feature_row=row,
+            regime=regime,
+            score=score,
+            confidence=conf,
+            drivers=model_drivers,
+            expected_volatility=float(row.get("realized_vol", 0.0)),
+        )
+        sig = self.risk.apply(
+            signal=sig,
+            regime=regime,
+            reliability=px_res.reliability,
+            liquidity_ok=True,
+            event_risk=event_risk,
+        )
+
+        payload = self.output.to_json_payload(sig)
+        summary = self.output.trader_summary(sig)
+        dashboard = self.output.dashboard_payload(sig)
+        hooks = self.integration.export_scores(feat, regime)
+
+        return {
+            "signal": payload,
+            "summary": summary,
+            "dashboard": dashboard,
+            "integration_hooks": hooks,
+            "source_usage": [u.__dict__ for u in self.router.usage_log],
+        }
+
+    def run_relative_value(self, asset_a: str, asset_b: str, start: datetime, end: datetime) -> dict[str, Any]:
+        ctx_a = self._asset_context(asset_a, start, end)
+        ctx_b = self._asset_context(asset_b, start, end)
+        feat_a = ctx_a["features"]
+        feat_b = ctx_b["features"]
+
+        rv = self.rv_model.generate(
+            asset_a=asset_a,
+            asset_b=asset_b,
+            prices_a=ctx_a["clean"]["close"],
+            prices_b=ctx_b["clean"]["close"],
+            features_a=feat_a,
+            features_b=feat_b,
+        )
+
+        regime = ctx_a["regime"] if ctx_a["regime"].stability >= ctx_b["regime"].stability else ctx_b["regime"]
+        driver_vals = [v for k, v in rv.drivers.items() if k != "hedge_ratio"]
+        signs = [np.sign(v) for v in driver_vals if abs(v) > 1e-12]
+        model_agreement = float(abs(sum(signs)) / len(signs)) if signs else 0.0
+        feature_agreement = float((feat_a.iloc[-1] - feat_b.iloc[-1] > 0).mean())
+
+        rel_map = {"high": 1.0, "medium": 0.8, "low": 0.6}
+        q_a = rel_map.get(ctx_a["price"].reliability, 0.6)
+        q_b = rel_map.get(ctx_b["price"].reliability, 0.6)
+        data_quality = float((q_a + q_b) / 2.0)
+
+        volatility = float(min(1.0, rv.expected_volatility))
+        event_risk = float(
+            min(
+                1.0,
+                (
+                    float(feat_a.iloc[-1].get("rbi_intervention_proxy", 0.0))
+                    + float(feat_b.iloc[-1].get("rbi_intervention_proxy", 0.0))
+                )
+                / 2.0,
+            )
+        )
+
+        conf = self.confidence.compute(
+            feature_agreement=feature_agreement,
+            model_agreement=model_agreement,
+            regime_stability=regime.stability,
+            data_quality=data_quality,
+            volatility=volatility,
+            event_risk=event_risk,
+        )
+
+        sig = self.signals.relative_value_signal(
+            long_asset=rv.long_asset,
+            short_asset=rv.short_asset,
+            regime=regime,
+            score=rv.score,
+            confidence=conf,
+            drivers=rv.drivers,
+            expected_volatility=rv.expected_volatility,
+        )
+        reliability = "high" if min(q_a, q_b) >= 1.0 else "medium" if min(q_a, q_b) >= 0.8 else "low"
+        sig = self.risk.apply(
+            signal=sig,
+            regime=regime,
+            reliability=reliability,
+            liquidity_ok=True,
+            event_risk=event_risk,
+        )
+
+        return {
+            "relative_value_pair": f"{asset_a}:{asset_b}",
+            "long": rv.long_asset,
+            "short": rv.short_asset,
+            "spread_score": rv.score,
+            "hedge_ratio": rv.hedge_ratio,
+            "spread_zscore": rv.spread_zscore,
+            "signal": self.output.to_json_payload(sig),
+            "summary": self.output.trader_summary(sig),
+            "dashboard": self.output.dashboard_payload(sig),
+        }
@@ -0,0 +1,3 @@
+from options_quant_engine.evaluation.engine import Evaluator
+
+__all__ = ["Evaluator"]
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.schemas import EvaluationReport
+
+
+class Evaluator:
+    def evaluate(
+        self,
+        signals: pd.DataFrame,
+        forward_returns: pd.Series,
+        regimes: pd.Series,
+        feature_importance: dict[str, float],
+    ) -> EvaluationReport:
+        if signals.empty or forward_returns.empty:
+            return EvaluationReport(
+                generated_at=datetime.now(timezone.utc).isoformat(),
+                hit_rate=0.0,
+                avg_forward_return=0.0,
+                regime_performance={},
+                signal_decay={},
+                feature_importance=feature_importance,
+                drift_flags=["insufficient_data"],
+                calibration_score=0.0,
+            )
+
+        aligned = signals.join(forward_returns.rename("fwd"), how="inner")
+        aligned = aligned.join(regimes.rename("regime"), how="left").fillna("unknown")
+        pred = np.sign(aligned["signal_strength"] * aligned.get("signal_sign", 1.0))
+        realized = np.sign(aligned["fwd"])
+        hit_rate = float((pred == realized).mean())
+        avg_ret = float(aligned["fwd"].mean())
+
+        regime_perf = {
+            r: float(v)
+            for r, v in aligned.groupby("regime")["fwd"].mean().to_dict().items()
+        }
+
+        signal_decay = {
+            "1d": float(aligned["fwd"].mean()),
+            "5d": float(aligned["fwd"].rolling(5).mean().dropna().mean() if len(aligned) >= 5 else 0.0),
+            "20d": float(aligned["fwd"].rolling(20).mean().dropna().mean() if len(aligned) >= 20 else 0.0),
+        }
+
+        drift = []
+        if abs(avg_ret) < 1e-4:
+            drift.append("low_signal_edge")
+        if hit_rate < 0.48:
+            drift.append("hit_rate_deterioration")
+
+        conf = aligned.get("confidence", pd.Series(0.5, index=aligned.index))
+        calibration_score = float(np.clip(1.0 - abs(conf.mean() - hit_rate), 0.0, 1.0))
+
+        return EvaluationReport(
+            generated_at=datetime.now(timezone.utc).isoformat(),
+            hit_rate=hit_rate,
+            avg_forward_return=avg_ret,
+            regime_performance=regime_perf,
+            signal_decay=signal_decay,
+            feature_importance=feature_importance,
+            drift_flags=drift,
+            calibration_score=calibration_score,
+        )
@@ -0,0 +1,3 @@
+from options_quant_engine.features.pipeline import FeaturePipeline
+
+__all__ = ["FeaturePipeline"]
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any
+
+import pandas as pd
+
+FeatureFn = Callable[[pd.DataFrame, dict[str, Any]], pd.Series]
+
+
+class FeatureRegistry:
+    def __init__(self) -> None:
+        self._features: dict[str, FeatureFn] = {}
+
+    def register(self, name: str, fn: FeatureFn) -> None:
+        if name in self._features:
+            raise ValueError(f"Feature already registered: {name}")
+        self._features[name] = fn
+
+    def get(self, name: str) -> FeatureFn:
+        return self._features[name]
+
+    def names(self) -> list[str]:
+        return list(self._features.keys())
+
+
+registry = FeatureRegistry()
@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.features.base import registry
+
+
+def _close(df: pd.DataFrame) -> pd.Series:
+    if "close" in df.columns:
+        return df["close"]
+    numeric = df.select_dtypes(include="number")
+    if numeric.empty:
+        return pd.Series(0.0, index=df.index)
+    return numeric.iloc[:, 0]
+
+
+def returns(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    return _close(df).pct_change().fillna(0.0)
+
+
+def moving_averages(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["medium"]
+    c = _close(df)
+    return (c / c.rolling(w).mean() - 1.0).fillna(0.0)
+
+
+def breakouts(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["long"]
+    c = _close(df)
+    high = c.rolling(w).max()
+    low = c.rolling(w).min()
+    rng = (high - low).replace(0.0, np.nan)
+    return ((c - low) / rng).fillna(0.5)
+
+
+def trend_strength(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    w = cfg["windows"]["medium"]
+    r = returns(df, cfg)
+    return (r.rolling(w).mean() / (r.rolling(w).std() + 1e-9)).fillna(0.0)
+
+
+def momentum_multi_horizon(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    s = cfg["windows"]["short"]
+    m = cfg["windows"]["medium"]
+    l = cfg["windows"]["long"]
+    return ((c.pct_change(s) + c.pct_change(m) + c.pct_change(l)) / 3.0).fillna(0.0)
+
+
+def momentum_persistence(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return r.rolling(cfg["windows"]["short"]).apply(lambda x: float((x > 0).mean()), raw=False).fillna(0.5)
+
+
+def momentum_acceleration(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    m = momentum_multi_horizon(df, cfg)
+    return m.diff().fillna(0.0)
+
+
+def mean_reversion_zscore(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    w = cfg["windows"]["medium"]
+    m = c.rolling(w).mean()
+    s = c.rolling(w).std()
+    return ((c - m) / (s + 1e-9)).fillna(0.0)
+
+
+def bollinger_position(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    z = mean_reversion_zscore(df, cfg)
+    return (z / 2.0).clip(-1, 1)
+
+
+def realized_vol(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return (r.rolling(cfg["windows"]["medium"]).std() * np.sqrt(252)).fillna(0.0)
+
+
+def atr_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    c = _close(df)
+    return c.diff().abs().rolling(cfg["windows"]["short"]).mean().fillna(0.0)
+
+
+def vol_regime_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    vol = realized_vol(df, cfg)
+    return (vol / (vol.rolling(cfg["windows"]["long"]).mean() + 1e-9)).fillna(1.0)
+
+
+def carry_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    short_r = df.get("short_rate", pd.Series(0.05, index=df.index))
+    usd_r = df.get("usd_rate", pd.Series(0.045, index=df.index))
+    return (short_r - usd_r).fillna(0.0)
+
+
+def usd_strength_score(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return (-r.rolling(cfg["windows"]["medium"]).mean()).fillna(0.0)
+
+
+def risk_on_off_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    eq = df.get("equity_proxy", returns(df, cfg)).fillna(0.0)
+    vix = df.get("vix_proxy", realized_vol(df, cfg)).fillna(0.0)
+    return (eq - vix).fillna(0.0)
+
+
+def inr_crude_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    crude = df.get("crude_proxy", pd.Series(0.0, index=df.index))
+    return crude.pct_change().fillna(0.0)
+
+
+def em_stress_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    em = df.get("emfx_proxy", returns(df, cfg)).fillna(0.0)
+    return (-em.rolling(cfg["windows"]["medium"]).mean()).fillna(0.0)
+
+
+def inr_vol_clustering(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    r = returns(df, cfg)
+    return r.abs().rolling(cfg["windows"]["short"]).mean().fillna(0.0)
+
+
+def rbi_intervention_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    z = mean_reversion_zscore(df, cfg)
+    return (z.abs() > 2.0).astype(float)
+
+
+def cross_asset_bond_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    y = df.get("bond_yield_proxy", pd.Series(0.0, index=df.index))
+    return y.diff().fillna(0.0)
+
+
+def cross_asset_commodity_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    g = df.get("gold_proxy", pd.Series(0.0, index=df.index))
+    o = df.get("oil_proxy", pd.Series(0.0, index=df.index))
+    return (g.pct_change().fillna(0.0) + o.pct_change().fillna(0.0)) / 2.0
+
+
+def cross_asset_equity_proxy(df: pd.DataFrame, cfg: dict) -> pd.Series:
+    e = df.get("equity_proxy", pd.Series(0.0, index=df.index))
+    return e.pct_change().fillna(0.0)
+
+
+def register_builtin_features() -> None:
+    registry.register("returns", returns)
+    registry.register("moving_averages", moving_averages)
+    registry.register("breakouts", breakouts)
+    registry.register("trend_strength", trend_strength)
+    registry.register("momentum_multi_horizon", momentum_multi_horizon)
+    registry.register("momentum_persistence", momentum_persistence)
+    registry.register("momentum_acceleration", momentum_acceleration)
+    registry.register("mean_reversion_zscore", mean_reversion_zscore)
+    registry.register("bollinger_position", bollinger_position)
+    registry.register("realized_vol", realized_vol)
+    registry.register("atr_proxy", atr_proxy)
+    registry.register("vol_regime_proxy", vol_regime_proxy)
+    registry.register("carry_proxy", carry_proxy)
+    registry.register("usd_strength_score", usd_strength_score)
+    registry.register("risk_on_off_proxy", risk_on_off_proxy)
+    registry.register("inr_crude_proxy", inr_crude_proxy)
+    registry.register("em_stress_proxy", em_stress_proxy)
+    registry.register("inr_vol_clustering", inr_vol_clustering)
+    registry.register("rbi_intervention_proxy", rbi_intervention_proxy)
+    registry.register("cross_asset_bond_proxy", cross_asset_bond_proxy)
+    registry.register("cross_asset_commodity_proxy", cross_asset_commodity_proxy)
+    registry.register("cross_asset_equity_proxy", cross_asset_equity_proxy)
@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Any
+
+import pandas as pd
+
+from options_quant_engine.features.base import registry
+from options_quant_engine.features.builtins import register_builtin_features
+
+
+class FeaturePipeline:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+        if not registry.names():
+            register_builtin_features()
+
+    def transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        feature_cfg = self.config.get("features", {})
+        out = pd.DataFrame(index=df.index)
+        for feature_name, enabled in feature_cfg.items():
+            if not enabled:
+                continue
+            if feature_name not in registry.names():
+                continue
+            out[feature_name] = registry.get(feature_name)(df, self.config)
+        return out.fillna(0.0)
@@ -0,0 +1,21 @@
+from options_quant_engine.ingestion.adapters import (
+    BreezeAdapter,
+    FreeFXAdapter,
+    MockAdapter,
+    NSEAdapter,
+    RBIAdapter,
+    ZerodhaAdapter,
+)
+from options_quant_engine.ingestion.base import BaseDataAdapter
+from options_quant_engine.ingestion.router import DataSourceRouter
+
+__all__ = [
+    "BaseDataAdapter",
+    "BreezeAdapter",
+    "ZerodhaAdapter",
+    "NSEAdapter",
+    "RBIAdapter",
+    "FreeFXAdapter",
+    "MockAdapter",
+    "DataSourceRouter",
+]
@@ -0,0 +1,298 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime
+import os
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.ingestion.base import BaseDataAdapter
+from options_quant_engine.ingestion.credentials import APICredentials, load_credentials
+from options_quant_engine.ingestion.http_client import HttpClient, RetryConfig
+
+
+def _mock_price_series(asset: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    steps = rng.normal(loc=0.0, scale=0.002, size=len(idx))
+    base = 80.0 if asset.endswith("INR") else 1.1
+    px = base * np.exp(np.cumsum(steps))
+    return pd.DataFrame({"close": px}, index=idx)
+
+
+def _mock_macro_series(key: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    values = rng.normal(loc=0.0, scale=1.0, size=len(idx))
+    return pd.DataFrame({key: values}, index=idx)
+
+
+def _mock_rate_series(asset: str, start: datetime, end: datetime, seed: int) -> pd.DataFrame:
+    idx = pd.date_range(start=start, end=end, freq="B")
+    rng = np.random.default_rng(seed)
+    rate = 0.05 + rng.normal(loc=0.0, scale=0.002, size=len(idx))
+    return pd.DataFrame({f"{asset}_rate": rate}, index=idx)
+
+
+def _retry_config_from_env() -> RetryConfig:
+    return RetryConfig(
+        timeout_sec=float(os.getenv("OQE_HTTP_TIMEOUT_SEC", "5.0")),
+        max_attempts=int(os.getenv("OQE_HTTP_MAX_ATTEMPTS", "3")),
+        backoff_sec=float(os.getenv("OQE_HTTP_BACKOFF_SEC", "0.5")),
+    )
+
+
+def _to_price_frame(payload: dict[str, Any], start: datetime, end: datetime) -> pd.DataFrame:
+    records = payload.get("data", [])
+    if not isinstance(records, list) or not records:
+        raise RuntimeError("Price payload missing 'data' list")
+    df = pd.DataFrame(records)
+    if "timestamp" in df.columns:
+        df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True, errors="coerce")
+        df = df.dropna(subset=["timestamp"]).set_index("timestamp")
+    elif "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce")
+        df = df.dropna(subset=["date"]).set_index("date")
+    else:
+        idx = pd.date_range(start=start, end=end, periods=len(df), tz="UTC")
+        df.index = idx
+    if "close" not in df.columns:
+        if "price" in df.columns:
+            df = df.rename(columns={"price": "close"})
+        else:
+            raise RuntimeError("Price payload missing close/price column")
+    return df[["close"]].sort_index()
+
+
+def _to_series_frame(payload: dict[str, Any], key: str, start: datetime, end: datetime) -> pd.DataFrame:
+    records = payload.get("data", [])
+    if not isinstance(records, list) or not records:
+        raise RuntimeError("Series payload missing 'data' list")
+    df = pd.DataFrame(records)
+    if "timestamp" in df.columns:
+        df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True, errors="coerce")
+        df = df.dropna(subset=["timestamp"]).set_index("timestamp")
+    elif "date" in df.columns:
+        df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce")
+        df = df.dropna(subset=["date"]).set_index("date")
+    else:
+        idx = pd.date_range(start=start, end=end, periods=len(df), tz="UTC")
+        df.index = idx
+    if key not in df.columns:
+        # Accept first numeric column as a tolerant API stub fallback.
+        numeric = df.select_dtypes(include="number")
+        if numeric.empty:
+            raise RuntimeError(f"Series payload missing '{key}'")
+        return numeric.iloc[:, :1].rename(columns={numeric.columns[0]: key})
+    return df[[key]].sort_index()
+
+
+def _live_enabled(env_key: str) -> bool:
+    return os.getenv(env_key, "false").lower() in {"1", "true", "yes", "on"}
+
+
+@dataclass
+class BreezeAdapter(BaseDataAdapter):
+    use_live: bool = False
+    base_url: str | None = None
+
+    def __post_init__(self) -> None:
+        self.use_live = self.use_live or _live_enabled("BREEZE_LIVE_ENABLED")
+        self.base_url = self.base_url or os.getenv("BREEZE_BASE_URL")
+        self.client = HttpClient(_retry_config_from_env())
+
+    def _creds(self) -> APICredentials:
+        return load_credentials("BREEZE", required=["API_KEY", "API_SECRET"], optional=["SESSION_TOKEN"])
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/price",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": creds.get("api_key", "")},
+            )
+            return _to_price_frame(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=1)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/macro",
+                params={"key": key, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": creds.get("api_key", "")},
+            )
+            return _to_series_frame(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=2)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Breeze live enabled but BREEZE_BASE_URL not configured")
+            creds = self._creds()
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/v1/rates",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={"X-API-KEY": creds.get("api_key", "")},
+            )
+            return _to_series_frame(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=3)
+
+    def health_check(self) -> bool:
+        if not self.use_live:
+            return True
+        return bool(self.base_url)
+
+
+@dataclass
+class ZerodhaAdapter(BaseDataAdapter):
+    use_live: bool = False
+    base_url: str | None = None
+
+    def __post_init__(self) -> None:
+        self.use_live = self.use_live or _live_enabled("ZERODHA_LIVE_ENABLED")
+        self.base_url = self.base_url or os.getenv("ZERODHA_BASE_URL")
+        self.client = HttpClient(_retry_config_from_env())
+
+    def _creds(self) -> APICredentials:
+        return load_credentials("ZERODHA", required=["API_KEY", "ACCESS_TOKEN"])
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            if not self.base_url:
+                raise RuntimeError("Zerodha live enabled but ZERODHA_BASE_URL not configured")
+            creds = self._creds()
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/instruments/historical",
+                params={"symbol": asset, "start": start.isoformat(), "end": end.isoformat()},
+                headers={
+                    "X-Kite-Version": "3",
+                    "Authorization": f"token {creds.get('api_key', '')}:{creds.get('access_token', '')}",
+                },
+            )
+            return _to_price_frame(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=4)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        raise NotImplementedError("Zerodha macro data not supported")
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        raise NotImplementedError("Zerodha rates not supported")
+
+    def health_check(self) -> bool:
+        if not self.use_live:
+            return True
+        return bool(self.base_url)
+
+
+class NSEAdapter(BaseDataAdapter):
+    def __init__(self, use_live: bool = False, base_url: str | None = None) -> None:
+        self.use_live = use_live or _live_enabled("NSE_LIVE_ENABLED")
+        self.base_url = base_url or os.getenv("NSE_BASE_URL", "https://www.nseindia.com")
+        self.client = HttpClient(_retry_config_from_env())
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/historical/foCPV",
+                params={"symbol": asset, "from": start.strftime("%d-%m-%Y"), "to": end.strftime("%d-%m-%Y")},
+                headers={"User-Agent": "options-quant-engine/0.1"},
+            )
+            return _to_price_frame(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=5)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/allIndices",
+                headers={"User-Agent": "options-quant-engine/0.1"},
+            )
+            return _to_series_frame(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=6)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/live-analysis-oi-spurts-underlyings",
+                headers={"User-Agent": "options-quant-engine/0.1"},
+            )
+            return _to_series_frame(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=7)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class RBIAdapter(BaseDataAdapter):
+    def __init__(self, use_live: bool = False, base_url: str | None = None) -> None:
+        self.use_live = use_live or _live_enabled("RBI_LIVE_ENABLED")
+        self.base_url = base_url or os.getenv("RBI_BASE_URL", "https://data.rbi.org.in")
+        self.client = HttpClient(_retry_config_from_env())
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/exchange-rate",
+                params={"pair": asset, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return _to_price_frame(payload, start, end)
+        return _mock_price_series(asset, start, end, seed=8)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/macro",
+                params={"series": key, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return _to_series_frame(payload, key, start, end)
+        return _mock_macro_series(key, start, end, seed=9)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        if self.use_live:
+            col = f"{asset}_rate"
+            payload = self.client.get_json(
+                f"{self.base_url.rstrip('/')}/api/policy-rates",
+                params={"asset": asset, "from": start.isoformat(), "to": end.isoformat()},
+            )
+            return _to_series_frame(payload, col, start, end)
+        return _mock_rate_series(asset, start, end, seed=10)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class FreeFXAdapter(BaseDataAdapter):
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_price_series(asset, start, end, seed=11)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_macro_series(key, start, end, seed=12)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_rate_series(asset, start, end, seed=13)
+
+    def health_check(self) -> bool:
+        return True
+
+
+class MockAdapter(BaseDataAdapter):
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_price_series(asset, start, end, seed=42)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_macro_series(key, start, end, seed=43)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        return _mock_rate_series(asset, start, end, seed=44)
+
+    def health_check(self) -> bool:
+        return True
@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from datetime import datetime
+
+import pandas as pd
+
+
+class BaseDataAdapter(ABC):
+    @abstractmethod
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> pd.DataFrame:
+        pass
+
+    @abstractmethod
+    def health_check(self) -> bool:
+        pass
@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class APICredentials:
+    values: dict[str, str]
+
+    def get(self, key: str, default: str | None = None) -> str | None:
+        return self.values.get(key, default)
+
+
+def load_credentials(prefix: str, required: list[str], optional: list[str] | None = None) -> APICredentials:
+    optional = optional or []
+    payload: dict[str, str] = {}
+
+    missing: list[str] = []
+    for key in required:
+        env_key = f"{prefix}_{key}"
+        value = os.getenv(env_key)
+        if not value:
+            missing.append(env_key)
+            continue
+        payload[key.lower()] = value
+
+    for key in optional:
+        env_key = f"{prefix}_{key}"
+        value = os.getenv(env_key)
+        if value:
+            payload[key.lower()] = value
+
+    if missing:
+        raise RuntimeError(f"Missing required credentials: {', '.join(missing)}")
+    return APICredentials(values=payload)
@@ -0,0 +1,38 @@
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Any
+
+import requests
+
+
+@dataclass
+class RetryConfig:
+    timeout_sec: float = 5.0
+    max_attempts: int = 3
+    backoff_sec: float = 0.5
+
+
+class HttpClient:
+    def __init__(self, config: RetryConfig) -> None:
+        self.config = config
+
+    def get_json(self, url: str, params: dict[str, Any] | None = None, headers: dict[str, str] | None = None) -> dict[str, Any]:
+        last_err = "unknown"
+        for attempt in range(1, self.config.max_attempts + 1):
+            try:
+                response = requests.get(url, params=params, headers=headers, timeout=self.config.timeout_sec)
+                response.raise_for_status()
+                data = response.json()
+                if not isinstance(data, dict):
+                    raise RuntimeError("Expected JSON object response")
+                return data
+            except Exception as exc:  # noqa: BLE001
+                last_err = str(exc)
+                if attempt < self.config.max_attempts:
+                    time.sleep(self.config.backoff_sec * attempt)
+
+        raise RuntimeError(
+            f"HTTP GET failed after {self.config.max_attempts} attempts for {url}: {last_err}"
+        )
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+
+import pandas as pd
+
+from options_quant_engine.ingestion.base import BaseDataAdapter
+from options_quant_engine.schemas import DataFetchResult, SourceUsageRecord
+from options_quant_engine.utils.logging import get_logger
+
+
+class DataSourceRouter:
+    def __init__(
+        self,
+        adapters: dict[str, BaseDataAdapter],
+        config: dict[str, Any],
+    ) -> None:
+        self.adapters = adapters
+        self.config = config
+        self.logger = get_logger(self.__class__.__name__)
+        self.usage_log: list[SourceUsageRecord] = []
+
+    def _get_candidate_sources(self, asset: str) -> list[str]:
+        source_cfg = self.config
+        mapped = source_cfg.get("asset_source_map", {}).get(asset)
+        if mapped:
+            return mapped
+        g10 = asset in {"EURUSD", "GBPUSD", "USDJPY", "AUDUSD", "USDCAD", "USDCHF"}
+        key = "g10" if g10 else "default"
+        return source_cfg.get("source_priority", {}).get(key, ["mock"])
+
+    def _is_enabled(self, source: str) -> bool:
+        return bool(self.config.get("enabled_sources", {}).get(source, False))
+
+    def _tag(self, source: str) -> tuple[str, int]:
+        reliability = self.config.get("reliability_tags", {}).get(source, "low")
+        latency_ms = int(self.config.get("latency_tags_ms", {}).get(source, 1000))
+        return reliability, latency_ms
+
+    def fetch_price_data(self, asset: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("price", asset, start, end)
+
+    def fetch_macro_data(self, key: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("macro", key, start, end)
+
+    def fetch_rate_data(self, asset: str, start: datetime, end: datetime) -> DataFetchResult:
+        return self._fetch_with_fallback("rate", asset, start, end)
+
+    def _fetch_with_fallback(
+        self,
+        usage_type: str,
+        key: str,
+        start: datetime,
+        end: datetime,
+    ) -> DataFetchResult:
+        candidates = self._get_candidate_sources(key)
+        last_err = "No source attempted"
+
+        for source in candidates:
+            if not self._is_enabled(source):
+                continue
+            adapter = self.adapters.get(source)
+            if adapter is None:
+                continue
+            try:
+                if usage_type == "price":
+                    data = adapter.fetch_price_data(key, start, end)
+                elif usage_type == "macro":
+                    data = adapter.fetch_macro_data(key, start, end)
+                else:
+                    data = adapter.fetch_rate_data(key, start, end)
+                reliability, latency_ms = self._tag(source)
+                self.usage_log.append(SourceUsageRecord(asset=key, source=source, usage_type=usage_type))
+                self.logger.info("Fetched %s data for %s via %s", usage_type, key, source)
+                return DataFetchResult(
+                    data=data,
+                    source=source,
+                    reliability=reliability,
+                    latency_ms=latency_ms,
+                )
+            except Exception as exc:  # noqa: BLE001
+                last_err = str(exc)
+                self.usage_log.append(
+                    SourceUsageRecord(
+                        asset=key,
+                        source=source,
+                        usage_type=usage_type,
+                        success=False,
+                        error=last_err,
+                    )
+                )
+                self.logger.warning(
+                    "Failed %s data fetch for %s via %s: %s",
+                    usage_type,
+                    key,
+                    source,
+                    last_err,
+                )
+
+        return DataFetchResult(
+            data=pd.DataFrame(),
+            source="none",
+            reliability="low",
+            latency_ms=9999,
+            success=False,
+            error=last_err,
+        )
@@ -0,0 +1,3 @@
+from options_quant_engine.integration.hooks import IntegrationHooks
+
+__all__ = ["IntegrationHooks"]
@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from options_quant_engine.schemas import RegimeState
+
+
+class IntegrationHooks:
+    def export_scores(self, features: pd.DataFrame, regime: RegimeState) -> dict[str, float]:
+        if features.empty:
+            return {
+                "usd_strength_score": 0.0,
+                "inr_stress_score": 0.0,
+                "carry_attractiveness": 0.0,
+                "fx_volatility_stress": 0.0,
+                "risk_on_off_score": 0.0,
+            }
+        row = features.iloc[-1]
+        return {
+            "usd_strength_score": float(row.get("usd_strength_score", 0.0)),
+            "inr_stress_score": float(row.get("em_stress_proxy", 0.0) + row.get("inr_vol_clustering", 0.0)),
+            "carry_attractiveness": float(row.get("carry_proxy", 0.0)),
+            "fx_volatility_stress": float(row.get("realized_vol", 0.0)),
+            "risk_on_off_score": float(row.get("risk_on_off_proxy", 0.0)),
+            "regime_stability": float(regime.stability),
+        }
@@ -0,0 +1,4 @@
+from options_quant_engine.models.ensemble import EnsembleModel
+from options_quant_engine.models.relative_value import RelativeValueModel
+
+__all__ = ["EnsembleModel", "RelativeValueModel"]
@@ -0,0 +1,44 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+
+class EnsembleModel:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def directional_score(self, row: pd.Series) -> tuple[float, dict[str, float], float]:
+        weights = self.config.get("directional", {}).get("model_weights", {})
+        component = {
+            "trend": float(row.get("trend_strength", 0.0)),
+            "momentum": float(row.get("momentum_multi_horizon", 0.0)),
+            "mean_reversion": -float(row.get("mean_reversion_zscore", 0.0)),
+            "carry": float(row.get("carry_proxy", 0.0)),
+            "macro": float(row.get("risk_on_off_proxy", 0.0)),
+        }
+        wsum = sum(float(weights.get(k, 0.0)) for k in component)
+        score = 0.0
+        if wsum > 0:
+            score = sum(component[k] * float(weights.get(k, 0.0)) for k in component) / wsum
+        normalized = float(np.tanh(score))
+        signs = [np.sign(v) for v in component.values() if abs(v) > 1e-12]
+        agreement = float((abs(sum(signs)) / len(signs))) if signs else 0.0
+        return normalized, component, agreement
+
+    def relative_value_score(self, row_a: pd.Series, row_b: pd.Series) -> tuple[float, dict[str, float], float]:
+        weights = self.config.get("relative_value", {}).get("model_weights", {})
+        spread = float(row_a.get("mean_reversion_zscore", 0.0) - row_b.get("mean_reversion_zscore", 0.0))
+        mom_diff = float(row_a.get("momentum_multi_horizon", 0.0) - row_b.get("momentum_multi_horizon", 0.0))
+        carry_diff = float(row_a.get("carry_proxy", 0.0) - row_b.get("carry_proxy", 0.0))
+        component = {"spread": spread, "momentum_diff": mom_diff, "carry_diff": carry_diff}
+        wsum = sum(float(weights.get(k, 0.0)) for k in component)
+        score = 0.0
+        if wsum > 0:
+            score = sum(component[k] * float(weights.get(k, 0.0)) for k in component) / wsum
+        normalized = float(np.tanh(score))
+        signs = [np.sign(v) for v in component.values() if abs(v) > 1e-12]
+        agreement = float((abs(sum(signs)) / len(signs))) if signs else 0.0
+        return normalized, component, agreement
@@ -0,0 +1,15 @@
+"""Placeholder for future model extensions.
+
+This module is intentionally lightweight in the starter phase. Future additions can
+include gradient boosting, probabilistic forecasts, and online learning modules.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass
+class ModelExtensionConfig:
+    enabled: bool = False
+    name: str = "baseline_extension"
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+
+
+@dataclass
+class RelativeValueResult:
+    score: float
+    hedge_ratio: float
+    spread_zscore: float
+    expected_volatility: float
+    drivers: dict[str, float]
+    long_asset: str
+    short_asset: str
+
+
+class RelativeValueModel:
+    def __init__(self, lookback: int = 60) -> None:
+        self.lookback = lookback
+
+    def generate(
+        self,
+        asset_a: str,
+        asset_b: str,
+        prices_a: pd.Series,
+        prices_b: pd.Series,
+        features_a: pd.DataFrame,
+        features_b: pd.DataFrame,
+    ) -> RelativeValueResult:
+        pa, pb = self._align(prices_a, prices_b)
+        la = np.log(pa)
+        lb = np.log(pb)
+
+        hedge_ratio = self._hedge_ratio(la.tail(self.lookback), lb.tail(self.lookback))
+        spread = la - hedge_ratio * lb
+
+        window = max(20, self.lookback // 2)
+        zscore = self._zscore(spread, window=window)
+        spread_z = float(zscore.iloc[-1]) if len(zscore) else 0.0
+
+        row_a = features_a.iloc[-1]
+        row_b = features_b.iloc[-1]
+        momentum_diff = float(row_a.get("momentum_multi_horizon", 0.0) - row_b.get("momentum_multi_horizon", 0.0))
+        carry_diff = float(row_a.get("carry_proxy", 0.0) - row_b.get("carry_proxy", 0.0))
+
+        # Mean-reversion spread score with feature differentials.
+        raw = -0.60 * spread_z + 0.25 * momentum_diff + 0.15 * carry_diff
+        score = float(np.tanh(raw))
+
+        if score >= 0:
+            long_asset, short_asset = asset_a, asset_b
+        else:
+            long_asset, short_asset = asset_b, asset_a
+
+        expected_vol = float(spread.diff().tail(window).std() * np.sqrt(252.0)) if len(spread) > 2 else 0.0
+        drivers = {
+            "spread_zscore": spread_z,
+            "momentum_diff": momentum_diff,
+            "carry_diff": carry_diff,
+            "hedge_ratio": float(hedge_ratio),
+        }
+        return RelativeValueResult(
+            score=abs(score),
+            hedge_ratio=float(hedge_ratio),
+            spread_zscore=spread_z,
+            expected_volatility=max(expected_vol, 0.0),
+            drivers=drivers,
+            long_asset=long_asset,
+            short_asset=short_asset,
+        )
+
+    @staticmethod
+    def _align(a: pd.Series, b: pd.Series) -> tuple[pd.Series, pd.Series]:
+        joined = pd.concat([a.rename("a"), b.rename("b")], axis=1).dropna()
+        return joined["a"], joined["b"]
+
+    @staticmethod
+    def _hedge_ratio(a: pd.Series, b: pd.Series) -> float:
+        denom = float(np.var(b))
+        if abs(denom) < 1e-12:
+            return 1.0
+        return float(np.cov(a, b)[0, 1] / denom)
+
+    @staticmethod
+    def _zscore(series: pd.Series, window: int) -> pd.Series:
+        mu = series.rolling(window).mean()
+        sigma = series.rolling(window).std()
+        return ((series - mu) / (sigma + 1e-9)).fillna(0.0)
@@ -0,0 +1,3 @@
+from options_quant_engine.outputs.formatter import OutputFormatter
+
+__all__ = ["OutputFormatter"]
@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from typing import Any
+
+from options_quant_engine.schemas import SignalPayload
+
+
+class OutputFormatter:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config.get("output", {})
+
+    def to_json_payload(self, signal: SignalPayload) -> dict[str, Any]:
+        p = self.config.get("precision", 4)
+        data = signal.to_dict()
+        data["signal_strength"] = round(float(data["signal_strength"]), p)
+        data["confidence"] = round(float(data["confidence"]), p)
+        data["expected_volatility"] = round(float(data["expected_volatility"]), p)
+        data["position_sizing_multiplier"] = round(float(data["position_sizing_multiplier"]), p)
+        return data
+
+    def trader_summary(self, signal: SignalPayload) -> str:
+        return (
+            f"{signal.asset} | {signal.signal_type} | {signal.signal_direction} | "
+            f"strength={signal.signal_strength:.2f} confidence={signal.confidence:.2f} "
+            f"action={signal.recommended_action} size={signal.position_sizing_multiplier:.2f} "
+            f"regime={signal.regime.get('combined', 'neutral')}"
+        )
+
+    def dashboard_payload(self, signal: SignalPayload) -> dict[str, Any]:
+        return {
+            "asset": signal.asset,
+            "score": signal.signal_strength,
+            "confidence": signal.confidence,
+            "action": signal.recommended_action,
+            "risk_flags": signal.risk_flags,
+            "regime": signal.regime,
+        }
@@ -0,0 +1,3 @@
+from options_quant_engine.preprocessing.cleaning import preprocess_market_data
+
+__all__ = ["preprocess_market_data"]
@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+import pandas as pd
+
+
+def preprocess_market_data(df: pd.DataFrame) -> pd.DataFrame:
+    if df.empty:
+        return df
+    out = df.copy().sort_index()
+    out = out[~out.index.duplicated(keep="last")]
+    out = out.ffill().dropna(how="all")
+    return out
@@ -0,0 +1,3 @@
+from options_quant_engine.regime.engine import RegimeEngine
+
+__all__ = ["RegimeEngine"]
@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.schemas import RegimeState
+
+
+class RegimeEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def detect(self, features: pd.DataFrame) -> RegimeState:
+        if features.empty:
+            return RegimeState("neutral", "normal", "neutral", "risk_on", "neutral", 0.0)
+
+        row = features.iloc[-1]
+        trend = self._trend_regime(row)
+        vol = self._vol_regime(features)
+        dollar = self._dollar_regime(row)
+        risk = self._risk_regime(row, vol)
+        combined = self._combine(trend, vol, dollar, risk)
+        stability = self._stability(features)
+        return RegimeState(trend, vol, dollar, risk, combined, stability)
+
+    def _trend_regime(self, row: pd.Series) -> str:
+        score = float(row.get("trend_strength", 0.0))
+        th = float(self.config.get("trend", {}).get("slope_threshold", 0.0005))
+        band = float(self.config.get("trend", {}).get("mr_band", 0.003))
+        if score > th:
+            return "trend"
+        if score < -band:
+            return "mean_reversion"
+        return "neutral"
+
+    def _vol_regime(self, features: pd.DataFrame) -> str:
+        vol = features.get("realized_vol", pd.Series(0.0, index=features.index)).fillna(0.0)
+        cur = float(vol.iloc[-1])
+        ql = float(vol.quantile(self.config.get("volatility", {}).get("low_quantile", 0.3)))
+        qh = float(vol.quantile(self.config.get("volatility", {}).get("high_quantile", 0.7)))
+        qs = float(vol.quantile(self.config.get("volatility", {}).get("stress_quantile", 0.9)))
+        if cur >= qs:
+            return "stress"
+        if cur >= qh:
+            return "high"
+        if cur <= ql:
+            return "low"
+        return "normal"
+
+    def _dollar_regime(self, row: pd.Series) -> str:
+        usd = float(row.get("usd_strength_score", 0.0))
+        band = float(self.config.get("dollar", {}).get("neutral_band", 0.2))
+        if usd > band:
+            return "strengthening"
+        if usd < -band:
+            return "weakening"
+        return "neutral"
+
+    def _risk_regime(self, row: pd.Series, vol_regime: str) -> str:
+        risk_score = float(row.get("risk_on_off_proxy", 0.0))
+        threshold = float(self.config.get("risk", {}).get("risk_off_equity_threshold", -0.01))
+        if vol_regime == "stress":
+            return "stressed"
+        if risk_score < threshold:
+            return "risk_off"
+        return "risk_on"
+
+    def _combine(self, trend: str, vol: str, dollar: str, risk: str) -> str:
+        if vol == "stress" or risk == "stressed":
+            return "defensive"
+        if trend == "trend" and risk == "risk_on":
+            return "pro_trend"
+        if trend == "mean_reversion" and risk != "stressed":
+            return "mean_revert"
+        if dollar == "strengthening":
+            return "usd_dominant"
+        return "neutral"
+
+    def _stability(self, features: pd.DataFrame) -> float:
+        cols = [c for c in ["trend_strength", "realized_vol", "usd_strength_score", "risk_on_off_proxy"] if c in features]
+        if not cols:
+            return 0.0
+        tail = features[cols].tail(10)
+        vol = float(tail.std().mean())
+        return float(np.clip(1.0 - vol, 0.0, 1.0))
@@ -0,0 +1,3 @@
+from options_quant_engine.risk.engine import RiskEngine
+
+__all__ = ["RiskEngine"]
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+from options_quant_engine.schemas import RegimeState, SignalPayload
+from options_quant_engine.utils.time import is_within_market_hours
+
+
+class RiskEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config.get("risk", {})
+
+    def apply(self, signal: SignalPayload, regime: RegimeState, reliability: str, liquidity_ok: bool, event_risk: float) -> SignalPayload:
+        penalties = self.config.get("data_reliability_penalty", {})
+        rel_mult = float(penalties.get(reliability, 0.6))
+        size = signal.signal_strength * signal.confidence * rel_mult
+
+        if regime.volatility in {"high", "stress"}:
+            size *= float(self.config.get("stressed_regime_multiplier", 0.5))
+            signal.risk_flags.append("high_volatility_regime")
+
+        if not liquidity_ok:
+            size *= float(self.config.get("illiquid_market_multiplier", 0.6))
+            signal.risk_flags.append("liquidity_constraint")
+
+        if event_risk > 0.6:
+            size *= 0.5
+            signal.risk_flags.append("event_risk_high")
+
+        if not self._market_open_now():
+            signal.risk_flags.append("outside_primary_market_hours")
+            size *= 0.8
+
+        signal.position_sizing_multiplier = max(0.0, min(size, 1.0))
+        signal.recommended_action = self._action(signal.position_sizing_multiplier)
+        return signal
+
+    def _market_open_now(self) -> bool:
+        now = datetime.now(timezone.utc)
+        open_utc = self.config.get("market_hours", {}).get("inr_derivatives_open_utc", "03:45")
+        close_utc = self.config.get("market_hours", {}).get("inr_derivatives_close_utc", "10:00")
+        return is_within_market_hours(now, open_utc, close_utc)
+
+    @staticmethod
+    def _action(size: float) -> str:
+        if size >= 0.75:
+            return "enter"
+        if size >= 0.45:
+            return "enter_small"
+        if size >= 0.25:
+            return "hold"
+        if size > 0.0:
+            return "reduce"
+        return "no_trade"
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+from datetime import datetime, timezone
+from typing import Any
+
+import pandas as pd
+
+
+@dataclass
+class DataFetchResult:
+    data: pd.DataFrame
+    source: str
+    reliability: str
+    latency_ms: int
+    success: bool = True
+    error: str | None = None
+
+
+@dataclass
+class RegimeState:
+    trend: str
+    volatility: str
+    dollar: str
+    risk: str
+    combined: str
+    stability: float
+
+
+@dataclass
+class SignalPayload:
+    engine: str
+    timestamp: str
+    asset: str
+    signal_type: str
+    signal_direction: str
+    signal_strength: float
+    confidence: float
+    regime: dict[str, Any]
+    expected_volatility: float
+    risk_flags: list[str]
+    drivers: dict[str, Any]
+    recommended_action: str
+    position_sizing_multiplier: float
+
+    def to_dict(self) -> dict[str, Any]:
+        return asdict(self)
+
+
+@dataclass
+class EvaluationReport:
+    generated_at: str
+    hit_rate: float
+    avg_forward_return: float
+    regime_performance: dict[str, float]
+    signal_decay: dict[str, float]
+    feature_importance: dict[str, float]
+    drift_flags: list[str]
+    calibration_score: float
+
+
+@dataclass
+class BacktestResult:
+    pair_level_returns: dict[str, float]
+    portfolio_return: float
+    max_drawdown: float
+    turnover: float
+    metrics: dict[str, float]
+
+
+@dataclass
+class SourceUsageRecord:
+    asset: str
+    source: str
+    usage_type: str
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    success: bool = True
+    error: str | None = None
@@ -0,0 +1,4 @@
+from options_quant_engine.signals.confidence import ConfidenceEngine
+from options_quant_engine.signals.engine import SignalEngine
+
+__all__ = ["SignalEngine", "ConfidenceEngine"]
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+
+class ConfidenceEngine:
+    def __init__(self, config: dict[str, Any]) -> None:
+        self.config = config
+
+    def compute(
+        self,
+        feature_agreement: float,
+        model_agreement: float,
+        regime_stability: float,
+        data_quality: float,
+        volatility: float,
+        event_risk: float,
+    ) -> float:
+        c = self.config.get("confidence", {})
+        agreement_w = float(c.get("agreement_weight", 0.25))
+        regime_w = float(c.get("regime_stability_weight", 0.20))
+        data_w = float(c.get("data_quality_weight", 0.20))
+        vol_w = float(c.get("volatility_penalty_weight", 0.20))
+        event_w = float(c.get("event_penalty_weight", 0.15))
+
+        agreement = 0.5 * (feature_agreement + model_agreement)
+        vol_penalty = np.clip(volatility, 0.0, 1.0)
+        event_penalty = np.clip(event_risk, 0.0, 1.0)
+
+        raw = (
+            agreement_w * agreement
+            + regime_w * np.clip(regime_stability, 0.0, 1.0)
+            + data_w * np.clip(data_quality, 0.0, 1.0)
+            - vol_w * vol_penalty
+            - event_w * event_penalty
+        )
+        return float(np.clip((raw + 1.0) / 2.0, 0.0, 1.0))
@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from options_quant_engine.schemas import RegimeState, SignalPayload
+
+
+class SignalEngine:
+    def __init__(self, engine_name: str = "options_quant_engine") -> None:
+        self.engine_name = engine_name
+
+    def directional_signal(
+        self,
+        asset: str,
+        feature_row: pd.Series,
+        regime: RegimeState,
+        score: float,
+        confidence: float,
+        drivers: dict[str, float],
+        expected_volatility: float,
+    ) -> SignalPayload:
+        direction = self._direction(asset, score)
+        return SignalPayload(
+            engine=self.engine_name,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+            asset=asset,
+            signal_type="directional",
+            signal_direction=direction,
+            signal_strength=float(np.clip(abs(score), 0.0, 1.0)),
+            confidence=float(np.clip(confidence, 0.0, 1.0)),
+            regime=regime.__dict__,
+            expected_volatility=float(max(0.0, expected_volatility)),
+            risk_flags=[],
+            drivers={k: float(v) for k, v in drivers.items()},
+            recommended_action="hold",
+            position_sizing_multiplier=0.0,
+        )
+
+    def relative_value_signal(
+        self,
+        long_asset: str,
+        short_asset: str,
+        regime: RegimeState,
+        score: float,
+        confidence: float,
+        drivers: dict[str, float],
+        expected_volatility: float,
+    ) -> SignalPayload:
+        direction = f"long_{long_asset}_vs_{short_asset}"
+        asset = f"{long_asset}:{short_asset}"
+        return SignalPayload(
+            engine=self.engine_name,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+            asset=asset,
+            signal_type="relative_value",
+            signal_direction=direction,
+            signal_strength=float(np.clip(abs(score), 0.0, 1.0)),
+            confidence=float(np.clip(confidence, 0.0, 1.0)),
+            regime=regime.__dict__,
+            expected_volatility=float(max(0.0, expected_volatility)),
+            risk_flags=[],
+            drivers={k: float(v) for k, v in drivers.items()},
+            recommended_action="hold",
+            position_sizing_multiplier=0.0,
+        )
+
+    def _direction(self, asset: str, score: float) -> str:
+        if asset.startswith("USD"):
+            return "long_usd" if score >= 0 else "short_usd"
+        return "long_base" if score >= 0 else "short_base"
@@ -0,0 +1,4 @@
+from options_quant_engine.utils.config import load_all_configs, load_yaml
+from options_quant_engine.utils.logging import get_logger
+
+__all__ = ["load_yaml", "load_all_configs", "get_logger"]
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+
+def load_yaml(path: str | Path) -> dict[str, Any]:
+    with Path(path).open("r", encoding="utf-8") as f:
+        data = yaml.safe_load(f) or {}
+    if not isinstance(data, dict):
+        raise ValueError(f"Config file must contain a mapping: {path}")
+    return data
+
+
+def load_all_configs(config_dir: str | Path) -> dict[str, dict[str, Any]]:
+    cfg_dir = Path(config_dir)
+    return {
+        "universe": load_yaml(cfg_dir / "universe.yaml"),
+        "data_sources": load_yaml(cfg_dir / "data_sources.yaml"),
+        "features": load_yaml(cfg_dir / "features.yaml"),
+        "regimes": load_yaml(cfg_dir / "regimes.yaml"),
+        "models": load_yaml(cfg_dir / "models.yaml"),
+        "risk": load_yaml(cfg_dir / "risk.yaml"),
+        "output": load_yaml(cfg_dir / "output.yaml"),
+    }
@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+import logging
+
+
+def get_logger(name: str) -> logging.Logger:
+    logger = logging.getLogger(name)
+    if not logger.handlers:
+        handler = logging.StreamHandler()
+        fmt = "%(asctime)s | %(name)s | %(levelname)s | %(message)s"
+        handler.setFormatter(logging.Formatter(fmt))
+        logger.addHandler(handler)
+        logger.setLevel(logging.INFO)
+    return logger
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from datetime import datetime, time, timezone
+
+
+def is_within_market_hours(
+    now_utc: datetime,
+    open_hhmm: str,
+    close_hhmm: str,
+) -> bool:
+    open_h, open_m = [int(x) for x in open_hhmm.split(":")]
+    close_h, close_m = [int(x) for x in close_hhmm.split(":")]
+    open_t = time(hour=open_h, minute=open_m)
+    close_t = time(hour=close_h, minute=close_m)
+    t = now_utc.astimezone(timezone.utc).time()
+    return open_t <= t <= close_t
@@ -0,0 +1,41 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "options-quant-engine"
+version = "0.1.0"
+description = "Modular, explainable, production-grade options quant engine"
+readme = "README.md"
+requires-python = ">=3.10"
+authors = [{ name = "Options Quant Team" }]
+dependencies = [
+  "numpy>=1.26",
+  "pandas>=2.2",
+  "PyYAML>=6.0",
+  "scipy>=1.12",
+  "requests>=2.32"
+]
+
+[project.optional-dependencies]
+dev = [
+  "pytest>=8.0",
+  "pytest-cov>=5.0",
+  "ruff>=0.5"
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["options_quant_engine*"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"
+
+[tool.ruff]
+line-length = 100
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "B", "UP"]
+ignore = ["E501"]
--- a/Show More
+++ b/Show More