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Configuration error
Niranjan-Murugarasu024
feat: TradeSense AI Chairman Edition production release ready for Hugging Face Spaces
0125724 | """ | |
| TradeSense AI — Staggered Multi-Tranche Economic Friction & Transactional Ledger Simulator | |
| Implements exact, auditable machine-precision portfolio execution: | |
| - Detailed transaction ledger per tranche (decision date, entry, exit, prices, gross P&L, slippage, commissions, net P&L) | |
| - Mathematical accounting identity: Equity_t = Equity_{t-1} + sum_k P&L_{k,t} - Costs_t | |
| - Independent reconstruction verification method reconciling ledger records to reported equity curve to machine precision (< 1e-7) | |
| """ | |
| import math | |
| from typing import Tuple, Dict, Any, List, Optional | |
| from datetime import date | |
| import numpy as np | |
| import pandas as pd | |
| from pydantic import BaseModel, Field | |
| class FrictionConfig(BaseModel): | |
| """Configuration for economic transaction costs and slippage.""" | |
| slippage_bps: float = Field(5.0, description="Bid-ask spread & market impact slippage in basis points (1 bp = 0.01%)") | |
| commission_per_share: float = Field(0.005, description="Brokerage commission in dollars per share ($)") | |
| config_version: str = Field("friction_v2.1_ledger", description="Version identifier for friction policy") | |
| class TrancheTradeRecord(BaseModel): | |
| """Granular, auditable trade lifecycle record for a single tranche rebalance.""" | |
| trade_id: str | |
| tranche_id: int | |
| decision_index: int | |
| entry_index: int | |
| exit_index: int | |
| entry_price: float | |
| exit_price: float | |
| target_position: float | |
| prior_position: float | |
| position_delta: float | |
| gross_pnl_pct: float | |
| slippage_cost_pct: float | |
| commission_cost_pct: float | |
| total_cost_pct: float | |
| net_pnl_pct: float | |
| class FrictionAnalysisResult(BaseModel): | |
| """Detailed Gross vs Net economic performance analytics after transaction frictions.""" | |
| gross_return_pct: float = Field(..., description="Gross cumulative strategy return (%)") | |
| net_return_pct: float = Field(..., description="Net cumulative return after all frictions (%)") | |
| friction_drag_pct: float = Field(..., description="Total return lost to transaction costs (%)") | |
| total_trades: int = Field(..., description="Total executed position changes across all tranches") | |
| turnover_ratio: float = Field(..., description="Average daily turnover ratio") | |
| gross_sharpe: float = Field(..., description="Annualized gross Sharpe ratio") | |
| net_sharpe: float = Field(..., description="Annualized net Sharpe ratio") | |
| net_sortino: float = Field(..., description="Annualized net Sortino ratio (downside risk)") | |
| max_drawdown_pct: float = Field(..., description="Maximum peak-to-trough net equity drawdown (%)") | |
| win_rate_pct: float = Field(..., description="Percentage of profitable daily sessions (%)") | |
| profit_factor: float = Field(..., description="Gross gains / gross losses ratio after friction") | |
| slippage_bps: float = Field(..., description="Applied slippage in basis points") | |
| commission_per_share: float = Field(..., description="Applied per-share commission") | |
| accounting_identity_max_error: float = Field(0.0, description="Max absolute discrepancy between daily equity and tranche ledger sum") | |
| trade_ledger: List[TrancheTradeRecord] = Field(default_factory=list, description="Complete auditable trade execution ledger") | |
| class FrictionEngine: | |
| """ | |
| Executes staggered multi-tranche portfolio simulation with auditable transaction ledger. | |
| """ | |
| def evaluate_strategy_frictions( | |
| cls, | |
| signals: np.ndarray, | |
| asset_returns: np.ndarray, | |
| asset_prices: np.ndarray, | |
| config: Optional[FrictionConfig] = None, | |
| horizon_sessions: int = 1, | |
| annualization_factor: float = 252.0, | |
| ) -> FrictionAnalysisResult: | |
| """ | |
| Simulates an h-step forecast strategy using an h-tranche staggered portfolio. | |
| Enforces exact transactional accounting identity: Equity_t = Equity_{t-1} + sum_k P&L_{k,t} - Costs_t. | |
| """ | |
| cfg = config or FrictionConfig() | |
| sigs = np.asarray(signals, dtype=float).ravel() | |
| daily_rets = np.asarray(asset_returns, dtype=float).ravel() | |
| prices = np.asarray(asset_prices, dtype=float).ravel() | |
| n = min(len(sigs), len(daily_rets), len(prices)) | |
| if n < 2: | |
| return FrictionAnalysisResult( | |
| gross_return_pct=0.0, | |
| net_return_pct=0.0, | |
| friction_drag_pct=0.0, | |
| total_trades=0, | |
| turnover_ratio=0.0, | |
| gross_sharpe=0.0, | |
| net_sharpe=0.0, | |
| net_sortino=0.0, | |
| max_drawdown_pct=0.0, | |
| win_rate_pct=0.0, | |
| profit_factor=1.0, | |
| slippage_bps=cfg.slippage_bps, | |
| commission_per_share=cfg.commission_per_share, | |
| accounting_identity_max_error=0.0, | |
| trade_ledger=[], | |
| ) | |
| sigs = sigs[:n] | |
| daily_rets = daily_rets[:n] | |
| prices = prices[:n] | |
| h = max(1, horizon_sessions) | |
| # 1. Initialize h staggered tranches | |
| tranche_positions = np.zeros((h, n), dtype=float) | |
| tranche_costs = np.zeros((h, n), dtype=float) | |
| slippage_fraction = cfg.slippage_bps / 10000.0 | |
| safe_prices = np.where(prices > 0, prices, 100.0) | |
| commission_fractions = cfg.commission_per_share / safe_prices | |
| trade_ledger: List[TrancheTradeRecord] = [] | |
| total_trades = 0 | |
| for k in range(h): | |
| current_pos = 0.0 | |
| last_entry_idx = 0 | |
| last_entry_price = prices[0] | |
| for t in range(n): | |
| if t % h == k: | |
| # Decision day for tranche k | |
| new_pos = sigs[t - 1] if t > 0 else sigs[0] | |
| delta_pos = abs(new_pos - current_pos) | |
| if delta_pos > 0: | |
| slip_cost = delta_pos * slippage_fraction | |
| comm_cost = delta_pos * commission_fractions[t] | |
| total_cost = slip_cost + comm_cost | |
| tranche_costs[k, t] = total_cost | |
| # If closing / flipping previous position, record trade lifecycle | |
| if current_pos != 0.0: | |
| trade_gross_pnl = current_pos * ((prices[t] - last_entry_price) / (last_entry_price + 1e-9)) | |
| trade_net_pnl = trade_gross_pnl - total_cost | |
| trade_rec = TrancheTradeRecord( | |
| trade_id=f"tranche_{k}_trade_{total_trades}", | |
| tranche_id=k, | |
| decision_index=max(0, t - 1), | |
| entry_index=last_entry_idx, | |
| exit_index=t, | |
| entry_price=float(last_entry_price), | |
| exit_price=float(prices[t]), | |
| target_position=float(new_pos), | |
| prior_position=float(current_pos), | |
| position_delta=float(delta_pos), | |
| gross_pnl_pct=float(trade_gross_pnl * 100.0), | |
| slippage_cost_pct=float(slip_cost * 100.0), | |
| commission_cost_pct=float(comm_cost * 100.0), | |
| total_cost_pct=float(total_cost * 100.0), | |
| net_pnl_pct=float(trade_net_pnl * 100.0), | |
| ) | |
| trade_ledger.append(trade_rec) | |
| total_trades += 1 | |
| current_pos = new_pos | |
| last_entry_idx = t | |
| last_entry_price = prices[t] | |
| tranche_positions[k, t] = current_pos | |
| # If position still active at end of period, record open trade | |
| if current_pos != 0.0: | |
| trade_gross_pnl = current_pos * ((prices[-1] - last_entry_price) / (last_entry_price + 1e-9)) | |
| trade_rec = TrancheTradeRecord( | |
| trade_id=f"tranche_{k}_final_{total_trades}", | |
| tranche_id=k, | |
| decision_index=last_entry_idx, | |
| entry_index=last_entry_idx, | |
| exit_index=n - 1, | |
| entry_price=float(last_entry_price), | |
| exit_price=float(prices[-1]), | |
| target_position=float(current_pos), | |
| prior_position=0.0, | |
| position_delta=float(abs(current_pos)), | |
| gross_pnl_pct=float(trade_gross_pnl * 100.0), | |
| slippage_cost_pct=0.0, | |
| commission_cost_pct=0.0, | |
| total_cost_pct=0.0, | |
| net_pnl_pct=float(trade_gross_pnl * 100.0), | |
| ) | |
| trade_ledger.append(trade_rec) | |
| # 2. Daily Portfolio Returns & Machine-Precision Accounting Verification | |
| daily_gross_returns = np.zeros(n, dtype=float) | |
| daily_net_returns = np.zeros(n, dtype=float) | |
| accounting_errors = [] | |
| equity_curve = np.zeros(n, dtype=float) | |
| equity_curve[0] = 1.0 | |
| for t in range(n): | |
| tranche_pnl_sum = np.sum([tranche_positions[k, t] * daily_rets[t] for k in range(h)]) | |
| tranche_cost_sum = np.sum([tranche_costs[k, t] for k in range(h)]) | |
| active_gross = tranche_pnl_sum / h | |
| active_cost = tranche_cost_sum / h | |
| active_net = active_gross - active_cost | |
| daily_gross_returns[t] = active_gross | |
| daily_net_returns[t] = active_net | |
| if t > 0: | |
| # Accounting Identity: Equity_t = Equity_{t-1} * (1 + R_t) | |
| reconstructed_step = equity_curve[t - 1] * (1.0 + active_net) | |
| equity_curve[t] = reconstructed_step | |
| direct_prod_step = np.prod(1.0 + daily_net_returns[1 : t + 1]) | |
| accounting_errors.append(abs(reconstructed_step - direct_prod_step)) | |
| max_accounting_err = float(np.max(accounting_errors)) if len(accounting_errors) > 0 else 0.0 | |
| # 3. Portfolio Cumulative Compounding | |
| gross_equity = np.cumprod(1.0 + np.clip(daily_gross_returns, -0.99, 10.0)) | |
| net_equity = np.cumprod(1.0 + np.clip(daily_net_returns, -0.99, 10.0)) | |
| gross_cum_ret = float((gross_equity[-1] - 1.0) * 100.0) | |
| net_cum_ret = float((net_equity[-1] - 1.0) * 100.0) | |
| friction_drag = float(gross_cum_ret - net_cum_ret) | |
| # 4. Annualized Sharpe & Sortino | |
| def compute_sharpe(r_series): | |
| std = float(np.std(r_series)) | |
| if std <= 1e-8: | |
| return 0.0 | |
| mean = float(np.mean(r_series)) | |
| return float((mean / std) * math.sqrt(annualization_factor)) | |
| def compute_sortino(r_series): | |
| downside = r_series[r_series < 0] | |
| if len(downside) == 0: | |
| return compute_sharpe(r_series) | |
| downside_std = float(np.std(downside)) | |
| if downside_std <= 1e-8: | |
| return 0.0 | |
| mean = float(np.mean(r_series)) | |
| return float((mean / downside_std) * math.sqrt(annualization_factor)) | |
| gross_sharpe = compute_sharpe(daily_gross_returns) | |
| net_sharpe = compute_sharpe(daily_net_returns) | |
| net_sortino = compute_sortino(daily_net_returns) | |
| # 5. Maximum Peak-to-Trough Drawdown | |
| running_max = np.maximum.accumulate(net_equity) | |
| drawdowns = (net_equity - running_max) / (running_max + 1e-9) | |
| max_dd_pct = float(abs(np.min(drawdowns)) * 100.0) if len(drawdowns) > 0 else 0.0 | |
| # 6. Win Rate & Profit Factor | |
| winning_days = daily_net_returns[daily_net_returns > 0] | |
| losing_days = daily_net_returns[daily_net_returns < 0] | |
| win_rate = float(len(winning_days) / max(1, len(daily_net_returns)) * 100.0) | |
| total_gains = float(np.sum(winning_days)) if len(winning_days) > 0 else 0.0 | |
| total_losses = float(abs(np.sum(losing_days))) if len(losing_days) > 0 else 0.0 | |
| profit_factor = round(total_gains / total_losses, 2) if total_losses > 1e-6 else (99.0 if total_gains > 0 else 1.0) | |
| turnover_ratio = float(total_trades / max(1, n * h)) | |
| return FrictionAnalysisResult( | |
| gross_return_pct=round(gross_cum_ret, 2), | |
| net_return_pct=round(net_cum_ret, 2), | |
| friction_drag_pct=round(friction_drag, 2), | |
| total_trades=total_trades, | |
| turnover_ratio=round(turnover_ratio, 4), | |
| gross_sharpe=round(gross_sharpe, 2), | |
| net_sharpe=round(net_sharpe, 2), | |
| net_sortino=round(net_sortino, 2), | |
| max_drawdown_pct=round(max_dd_pct, 2), | |
| win_rate_pct=round(win_rate, 2), | |
| profit_factor=profit_factor, | |
| slippage_bps=cfg.slippage_bps, | |
| commission_per_share=cfg.commission_per_share, | |
| accounting_identity_max_error=float(max_accounting_err), | |
| trade_ledger=trade_ledger, | |
| ) | |