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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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rdagent/components/backtesting/__init__.py
+9 -9
View File
@@ -5,18 +5,18 @@ from .risk_management import CorrelationAnalyzer, PortfolioOptimizer, AdvancedRi
from .vbt_backtest import (
DEFAULT_BARS_PER_YEAR,
DEFAULT_TXN_COST_BPS,
FTMO_INITIAL_CAPITAL,
FTMO_MAX_DAILY_LOSS,
FTMO_MAX_TOTAL_LOSS,
FTMO_MAX_LEVERAGE,
FTMO_RISK_PER_TRADE,
INITIAL_CAPITAL,
MAX_DAILY_LOSS,
MAX_TOTAL_LOSS,
MAX_LEVERAGE,
RISK_PER_TRADE,
OOS_START_DEFAULT,
WF_IS_YEARS,
WF_OOS_YEARS,
WF_STEP_YEARS,
backtest_from_forward_returns,
backtest_signal,
backtest_signal_ftmo,
backtest_signal_risk,
monte_carlo_trade_pvalue,
walk_forward_rolling,
)
@@ -24,10 +24,10 @@ from .vbt_backtest import (
__all__ = [
'BacktestMetrics', 'FactorBacktester', 'ResultsDatabase',
'CorrelationAnalyzer', 'PortfolioOptimizer', 'AdvancedRiskManager',
'backtest_signal', 'backtest_signal_ftmo', 'backtest_from_forward_returns',
'backtest_signal', 'backtest_signal_risk', 'backtest_from_forward_returns',
'monte_carlo_trade_pvalue', 'walk_forward_rolling',
'DEFAULT_BARS_PER_YEAR', 'DEFAULT_TXN_COST_BPS',
'FTMO_INITIAL_CAPITAL', 'FTMO_MAX_DAILY_LOSS', 'FTMO_MAX_TOTAL_LOSS',
'FTMO_MAX_LEVERAGE', 'FTMO_RISK_PER_TRADE', 'OOS_START_DEFAULT',
'INITIAL_CAPITAL', 'MAX_DAILY_LOSS', 'MAX_TOTAL_LOSS',
'MAX_LEVERAGE', 'RISK_PER_TRADE', 'OOS_START_DEFAULT',
'WF_IS_YEARS', 'WF_OOS_YEARS', 'WF_STEP_YEARS',
]
rdagent/components/backtesting/vbt_backtest.py
+45 -45
View File
@@ -38,15 +38,15 @@ DEFAULT_TXN_COST_BPS = 2.14
DEFAULT_BARS_PER_YEAR = 252 * 1440 # 252 trading days * 1440 min/day = 362,880
EXTREME_BAR_THRESHOLD = 0.05 # |ret| > 5% on a single 1-min bar → suspicious
# FTMO 100k account rules (enforced in backtest_signal when ftmo=True)
FTMO_INITIAL_CAPITAL = 100_000.0
FTMO_MAX_DAILY_LOSS = 0.05 # 5% of initial → block new trades rest of day
FTMO_MAX_TOTAL_LOSS = 0.10 # 10% of initial → simulation ends
# RiskMgmt 100k account rules (enforced in backtest_signal when riskmgmt=True)
INITIAL_CAPITAL = 100_000.0
MAX_DAILY_LOSS = 0.05 # 5% of initial → block new trades rest of day
MAX_TOTAL_LOSS = 0.10 # 10% of initial → simulation ends
# Risk-based position sizing: 1.5% equity risk per trade, 10-pip stop, max 1:30 leverage
FTMO_RISK_PER_TRADE = 0.015
FTMO_STOP_PIPS = 10
FTMO_PIP = 0.0001
FTMO_MAX_LEVERAGE = 30
RISK_PER_TRADE = 0.015
STOP_PIPS = 10
PIP_SIZE = 0.0001
MAX_LEVERAGE = 30
def _compute_trade_pnl(position: pd.Series, strategy_returns: pd.Series) -> pd.Series:
@@ -274,31 +274,31 @@ def backtest_signal(
return result
def _apply_ftmo_mask(
def _apply_risk_mask(
signal: pd.Series,
close: pd.Series,
leverage: float,
txn_cost_bps: float,
) -> tuple[pd.Series, dict]:
"""
Apply FTMO daily/total loss rules to a signal series.
Apply RiskMgmt daily/total loss rules to a signal series.
Returns a masked signal (positions zeroed after each limit breach) and
a dict of FTMO compliance metrics.
a dict of RiskMgmt compliance metrics.
"""
txn_cost = txn_cost_bps / 10_000.0
position = signal.shift(1).fillna(0) * leverage
bar_ret = close.pct_change().fillna(0)
equity = FTMO_INITIAL_CAPITAL
peak_day = FTMO_INITIAL_CAPITAL
equity = INITIAL_CAPITAL
peak_day = INITIAL_CAPITAL
masked = signal.copy()
daily_breaches = 0
total_breached = False
total_breach_ts: pd.Timestamp | None = None
current_day = None
day_start_eq = FTMO_INITIAL_CAPITAL
day_start_eq = INITIAL_CAPITAL
pos_prev = 0.0
for ts, sig_i in signal.items():
@@ -319,24 +319,24 @@ def _apply_ftmo_mask(
masked.at[ts] = 0
continue
daily_loss = (equity - day_start_eq) / FTMO_INITIAL_CAPITAL
total_loss = (equity - FTMO_INITIAL_CAPITAL) / FTMO_INITIAL_CAPITAL
daily_loss = (equity - day_start_eq) / INITIAL_CAPITAL
total_loss = (equity - INITIAL_CAPITAL) / INITIAL_CAPITAL
if daily_loss < -FTMO_MAX_DAILY_LOSS:
if daily_loss < -MAX_DAILY_LOSS:
daily_breaches += 1
day_start_eq = -999 # block rest of day
masked.at[ts] = 0
if total_loss < -FTMO_MAX_TOTAL_LOSS:
if total_loss < -MAX_TOTAL_LOSS:
total_breached = True
total_breach_ts = ts
masked.at[ts] = 0
return masked, {
"ftmo_daily_breaches": daily_breaches,
"ftmo_total_breached": total_breached,
"ftmo_total_breach_ts": str(total_breach_ts) if total_breach_ts else None,
"ftmo_compliant": not total_breached and daily_breaches == 0,
"risk_daily_breaches": daily_breaches,
"risk_total_breached": total_breached,
"risk_total_breach_ts": str(total_breach_ts) if total_breach_ts else None,
"risk_compliant": not total_breached and daily_breaches == 0,
}
@@ -403,7 +403,7 @@ def walk_forward_rolling(
"""
Rolling walk-forward validation: multiple IS/OOS windows shifted by ``step_years``.
Each window runs an independent FTMO simulation on the IS and OOS slices.
Each window runs an independent RiskMgmt simulation on the IS and OOS slices.
Produces aggregate OOS statistics to measure cross-time consistency.
Returns
@@ -442,7 +442,7 @@ def walk_forward_rolling(
for mask, prefix in [(is_mask, "is"), (oos_mask, "oos")]:
close_s = close.loc[mask]
signal_s = signal.loc[mask]
masked_s, _ = _apply_ftmo_mask(signal_s, close_s, leverage, txn_cost_bps)
masked_s, _ = _apply_risk_mask(signal_s, close_s, leverage, txn_cost_bps)
r = backtest_signal(close=close_s, signal=masked_s,
txn_cost_bps=txn_cost_bps, bars_per_year=bars_per_year)
window[f"{prefix}_sharpe"] = r.get("sharpe", 0.0)
@@ -466,14 +466,14 @@ def walk_forward_rolling(
}
def backtest_signal_ftmo(
def backtest_signal_risk(
close: pd.Series,
signal: pd.Series,
txn_cost_bps: float = DEFAULT_TXN_COST_BPS,
eurusd_price: float = 1.10,
risk_pct: float = FTMO_RISK_PER_TRADE,
stop_pips: float = FTMO_STOP_PIPS,
max_leverage: float = FTMO_MAX_LEVERAGE,
risk_pct: float = RISK_PER_TRADE,
stop_pips: float = STOP_PIPS,
max_leverage: float = MAX_LEVERAGE,
bars_per_year: int = DEFAULT_BARS_PER_YEAR,
forward_returns: pd.Series | None = None,
oos_start: str | None = OOS_START_DEFAULT,
@@ -481,15 +481,15 @@ def backtest_signal_ftmo(
mc_n_permutations: int = 0,
) -> dict[str, Any]:
"""
FTMO-compliant backtest of a strategy signal on EUR/USD.
RiskMgmt-compliant backtest of a strategy signal on EUR/USD.
Applies on top of ``backtest_signal``:
- Realistic costs: default 2.14 bps (≈ 2.35 pip spread+slippage+commission)
- Risk-based position sizing: risk_pct equity per trade, stop_pips hard stop
- Max leverage cap: max_leverage (default 1:30, FTMO standard)
- FTMO daily loss limit (5%): positions zeroed rest of day after breach
- FTMO total loss limit (10%): all positions zeroed after breach
- FTMO-specific metrics added to result dict
- Max leverage cap: max_leverage (default 1:30, RiskMgmt standard)
- RiskMgmt daily loss limit (5%): positions zeroed rest of day after breach
- RiskMgmt total loss limit (10%): all positions zeroed after breach
- RiskMgmt-specific metrics added to result dict
- Walk-forward OOS split: IS metrics (before oos_start) + OOS metrics (after)
Parameters
@@ -507,7 +507,7 @@ def backtest_signal_ftmo(
stop_pips : float
Hard stop-loss distance in pips (default 10).
max_leverage : float
Maximum leverage (default 30 = FTMO 1:30).
Maximum leverage (default 30 = RiskMgmt 1:30).
oos_start : str or None
Start of out-of-sample period (ISO date). None disables OOS split.
wf_rolling : bool
@@ -518,11 +518,11 @@ def backtest_signal_ftmo(
When > 0, computes ``mc_pvalue``: fraction of permuted sequences whose
total return >= real total return. p < 0.05 indicates a genuine edge.
"""
stop_price = stop_pips * FTMO_PIP
stop_price = stop_pips * PIP_SIZE
leverage_by_risk = risk_pct / (stop_price / eurusd_price)
leverage = min(leverage_by_risk, max_leverage)
masked_signal, ftmo_metrics = _apply_ftmo_mask(signal, close, leverage, txn_cost_bps)
masked_signal, risk_metrics = _apply_risk_mask(signal, close, leverage, txn_cost_bps)
result = backtest_signal(
close=close,
@@ -532,14 +532,14 @@ def backtest_signal_ftmo(
forward_returns=forward_returns,
)
result.update(ftmo_metrics)
result["ftmo_leverage"] = round(leverage, 2)
result["ftmo_risk_pct"] = risk_pct
result["ftmo_stop_pips"] = stop_pips
result.update(risk_metrics)
result["risk_leverage"] = round(leverage, 2)
result["risk_risk_pct"] = risk_pct
result["risk_stop_pips"] = stop_pips
# Re-scale reported equity metrics to FTMO_INITIAL_CAPITAL
result["ftmo_end_equity"] = FTMO_INITIAL_CAPITAL * (1 + result.get("total_return", 0))
result["ftmo_monthly_profit"] = FTMO_INITIAL_CAPITAL * result.get("monthly_return", 0)
# Re-scale reported equity metrics to INITIAL_CAPITAL
result["risk_end_equity"] = INITIAL_CAPITAL * (1 + result.get("total_return", 0))
result["risk_monthly_profit"] = INITIAL_CAPITAL * result.get("monthly_return", 0)
# Walk-forward OOS split
if oos_start is not None:
@@ -551,9 +551,9 @@ def backtest_signal_ftmo(
if mask.sum() < 100:
return
close_s = close.loc[mask]
signal_s = signal.loc[mask] # raw signal, not masked — fresh FTMO sim per period
signal_s = signal.loc[mask] # raw signal, not masked — fresh RiskMgmt sim per period
fwd_split = forward_returns.loc[mask] if forward_returns is not None else None
masked_s, _ = _apply_ftmo_mask(signal_s, close_s, leverage, txn_cost_bps)
masked_s, _ = _apply_risk_mask(signal_s, close_s, leverage, txn_cost_bps)
split_result = backtest_signal(
close=close_s,
signal=masked_s,