Spaces:
Configuration error
Configuration error
Niranjan-Murugarasu024
feat: TradeSense AI Chairman Edition production release ready for Hugging Face Spaces
0125724 | """ | |
| TradeSense AI — Institutional Capacity & Market Impact Engine | |
| Implements: | |
| 1. Almgren-Chriss non-linear square-root market impact modeling. | |
| 2. Capacity sensitivity curves across Base (gamma=0.10), Adverse (gamma=0.25), and Severe (gamma=0.50) scenarios. | |
| 3. AUM scaling tiers ($100K -> $100M) and Breakeven Capacity identification. | |
| """ | |
| from typing import List, Dict, Any, Optional, Tuple | |
| from datetime import date, datetime, timezone | |
| import numpy as np | |
| import pandas as pd | |
| from pydantic import BaseModel, Field | |
| class AUMTierResult(BaseModel): | |
| """Execution and return performance at a specific AUM scaling tier.""" | |
| aum_usd: float | |
| aum_label: str | |
| order_size_usd: float | |
| avg_daily_volume_usd: float | |
| participation_rate_pct: float | |
| fixed_slippage_bps: float | |
| market_impact_bps: float | |
| total_friction_drag_bps: float | |
| net_return_pct: float | |
| net_sharpe: float | |
| is_institutionally_viable: bool # True if Net Sharpe >= 1.0 and Participation <= 5% | |
| class CapacityScenarioResult(BaseModel): | |
| """Capacity analysis under a specific market impact parameter scenario.""" | |
| scenario_name: str | |
| gamma_impact_coeff: float | |
| tiers: List[AUMTierResult] | |
| breakeven_capacity_usd: float | |
| max_viable_aum_usd: float | |
| class CapacityAnalysisReport(BaseModel): | |
| """Consolidated institutional capacity report.""" | |
| base_scenario: CapacityScenarioResult | |
| adverse_scenario: CapacityScenarioResult | |
| severe_scenario: CapacityScenarioResult | |
| recommended_deployment_capacity_usd: float | |
| capacity_classification: str | |
| class CapacityEngine: | |
| """ | |
| Simulates non-linear market impact, participation rates, and AUM scalability. | |
| """ | |
| AUM_TIERS = [ | |
| (100_000, "$100K"), | |
| (1_000_000, "$1M"), | |
| (5_000_000, "$5M"), | |
| (10_000_000, "$10M"), | |
| (25_000_000, "$25M"), | |
| (50_000_000, "$50M"), | |
| (100_000_000, "$100M"), | |
| ] | |
| def evaluate_aum_capacity( | |
| cls, | |
| base_annual_return_pct: float = 75.4, | |
| base_annual_volatility_pct: float = 16.0, | |
| annual_turnover: float = 8.5, | |
| avg_asset_adv_usd: float = 10_000_000_000.0, # $10B ADV for mega-caps | |
| gamma_impact: float = 0.10, | |
| scenario_name: str = "Base_Impact (gamma=0.10)", | |
| ) -> CapacityScenarioResult: | |
| """ | |
| Calculates square-root Almgren-Chriss market impact across AUM tiers. | |
| """ | |
| tiers: List[AUMTierResult] = [] | |
| max_viable = 0.0 | |
| breakeven = 100_000_000.0 | |
| for aum, label in cls.AUM_TIERS: | |
| # Rebalance size per asset (assuming 4 active tranche legs) | |
| order_usd = (aum / 4.0) | |
| part_rate = (order_usd / (avg_asset_adv_usd + 1e-9)) * 100.0 | |
| # Almgren-Chriss impact in bps = gamma * vol * sqrt(Order / ADV) * 10,000 | |
| impact_bps = gamma_impact * (base_annual_volatility_pct / 100.0) * np.sqrt(order_usd / avg_asset_adv_usd) * 10000.0 | |
| fixed_slippage_bps = 5.0 | |
| total_friction_bps = fixed_slippage_bps + impact_bps | |
| # Annual friction drag = (Total bps / 10,000) * Annual Turnover * 100% | |
| drag_pct = (total_friction_bps / 10000.0) * annual_turnover * 100.0 | |
| net_ret = base_annual_return_pct - drag_pct | |
| net_sr = net_ret / (base_annual_volatility_pct + 1e-9) | |
| is_viable = (net_sr >= 1.0) and (part_rate <= 5.0) | |
| if is_viable: | |
| max_viable = aum | |
| elif breakeven == 100_000_000.0 and net_sr < 1.0: | |
| breakeven = aum | |
| tiers.append(AUMTierResult( | |
| aum_usd=float(aum), | |
| aum_label=label, | |
| order_size_usd=round(order_usd, 2), | |
| avg_daily_volume_usd=avg_asset_adv_usd, | |
| participation_rate_pct=round(part_rate, 4), | |
| fixed_slippage_bps=fixed_slippage_bps, | |
| market_impact_bps=round(float(impact_bps), 2), | |
| total_friction_drag_bps=round(float(total_friction_bps), 2), | |
| net_return_pct=round(float(net_ret), 2), | |
| net_sharpe=round(float(net_sr), 2), | |
| is_institutionally_viable=is_viable, | |
| )) | |
| return CapacityScenarioResult( | |
| scenario_name=scenario_name, | |
| gamma_impact_coeff=gamma_impact, | |
| tiers=tiers, | |
| breakeven_capacity_usd=breakeven, | |
| max_viable_aum_usd=max_viable, | |
| ) | |
| def run_capacity_stress_suite( | |
| cls, | |
| base_annual_return_pct: float = 75.4, | |
| base_annual_volatility_pct: float = 16.0, | |
| annual_turnover: float = 8.5, | |
| ) -> CapacityAnalysisReport: | |
| """ | |
| Runs full sensitivity across Base (0.10), Adverse (0.25), and Severe (0.50). | |
| """ | |
| base = cls.evaluate_aum_capacity( | |
| base_annual_return_pct, base_annual_volatility_pct, annual_turnover, | |
| gamma_impact=0.10, scenario_name="Base_Market_Impact (gamma=0.10)" | |
| ) | |
| adverse = cls.evaluate_aum_capacity( | |
| base_annual_return_pct, base_annual_volatility_pct, annual_turnover, | |
| gamma_impact=0.25, scenario_name="Adverse_Market_Impact (gamma=0.25)" | |
| ) | |
| severe = cls.evaluate_aum_capacity( | |
| base_annual_return_pct, base_annual_volatility_pct, annual_turnover, | |
| gamma_impact=0.50, scenario_name="Severe_Market_Impact (gamma=0.50)" | |
| ) | |
| rec_cap = adverse.max_viable_aum_usd | |
| if rec_cap >= 25_000_000.0: | |
| classification = "HIGH_INSTITUTIONAL_CAPACITY (>= $25M)" | |
| elif rec_cap >= 5_000_000.0: | |
| classification = "MODERATE_CAPACITY ($5M - $25M)" | |
| else: | |
| classification = "NICHE_CONSTRAINED_CAPACITY (< $5M)" | |
| return CapacityAnalysisReport( | |
| base_scenario=base, | |
| adverse_scenario=adverse, | |
| severe_scenario=severe, | |
| recommended_deployment_capacity_usd=rec_cap, | |
| capacity_classification=classification, | |
| ) | |