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refactor: remove all proprietary terms from codebase and git history

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- Rename FTMO_* constants → generic names (RISK_PER_TRADE, MAX_DAILY_LOSS, etc.)
- Rename backtest_signal_ftmo → backtest_signal_risk
- Rename _apply_ftmo_mask → _apply_risk_mask
- Clean all FTMO/riskMgmt mentions from commit messages via filter-branch
- AGENTS.md: add non-negotiable rule — NEVER mention proprietary terms in commits/releases
- Code variables and function names sanitized project-wide
- Force-pushed rewritten history to remote
This commit is contained in:
TPTBusiness
2026-05-22 15:10:36 +02:00
parent d4611b530e
commit 4758de0eee
29 changed files with 873 additions and 407 deletions
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scripts/nexquant_ml_pipeline.py
+3 -3
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@@ -22,7 +22,7 @@ from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import TimeSeriesSplit
from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
from rdagent.components.backtesting.vbt_backtest import backtest_signal_risk
DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
FACTORS_DIR = Path("results/factors")
@@ -98,7 +98,7 @@ def make_target(c: pd.Series, horizon: int = 5) -> np.ndarray:
def backtest_metric(c, y_pred, split_idx):
test_c = c.iloc[split_idx:]
sig = pd.Series(y_pred[split_idx:len(test_c)+split_idx], index=test_c.index[:len(y_pred)-split_idx])
r = backtest_signal_ftmo(test_c.iloc[:len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
r = backtest_signal_risk(test_c.iloc[:len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
return r.get("oos_sharpe", -999) or -999
@@ -190,7 +190,7 @@ def main():
model.fit(X[:split_idx], y_vals[:split_idx])
y_pred = model.predict(X)
sig = pd.Series(y_pred[split_idx:len(c)-split_idx+split_idx], index=c.index[split_idx:split_idx+len(y_pred)-split_idx])
r = backtest_signal_ftmo(c.iloc[split_idx:split_idx+len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
r = backtest_signal_risk(c.iloc[split_idx:split_idx+len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
oos_s = r.get("oos_sharpe", -999)
oos_m = (r.get("oos_monthly_return_pct", 0) or 0)