Strategies the community has published.
Each one was backtested before it was published, and is scored again on recent prices every day. Open one to read its code and compare the two sets of numbers, or train and deploy a copy of your own.
- Published strategies
- 120
- Markets
- 7
- Timeframes
- 3
- Strategies built so far
- 23,022
- Last scored
At publish: the backtest numbers, frozen when the strategy was published. They never change.
Recent: the model is retrained every week and scored on a recent window it was not trained on. It shows whether the idea still works.
| # | Strategy | Market | RecentAt publish | Open | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| USDCAD 1h@silver-bull-130 | USDCAD 1h | +1.75%+13.96% | +13.96%+1.75% | 44.4%80.0% | 1.2224.32 | 3.96% | 95 | 195.76 | ||||||||||||||||||||
Signal Mix strategy on USD/CAD 1h. A compact, machine-selected indicator set; the classifier learns the entry rule directly from the features. Features: Z-score 20, ADX 20, Session/time features, RSI 14, Williams %R 21, CCI 14. Model: XGBoost; label horizon 1 bars, confidence threshold 0.52, direction short. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Only 5 recent trades, so the recent numbers are shown without colour: they are too few to read much into. Agents: POST /api/v1/fork/269strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:14:19
# Model : XGBoost
# Feature Eng. : Z-score 20, ADX 20, Session/time features, RSI 14, Williams %R 21, CCI 14
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : —
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCAD_1h.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- Z-score(20) of close ---
df["zscore_20"] = (close - close.rolling(20).mean()) / (close.rolling(20).std() + 1e-12)
df["zscore_20_chg"] = df["zscore_20"].diff(1)
# --- ADX(20) with +DI/-DI ---
_up = high.diff()
_dn = -low.diff()
_pdm = pd.Series(np.where((_up > _dn) & (_up > 0), _up, 0.0), index=df.index)
_ndm = pd.Series(np.where((_dn > _up) & (_dn > 0), _dn, 0.0), index=df.index)
_tr2 = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr2 = _tr2.ewm(alpha=1.0/20, adjust=False).mean()
_pdi = 100.0 * _pdm.ewm(alpha=1.0/20, adjust=False).mean() / (_atr2 + 1e-12)
_ndi = 100.0 * _ndm.ewm(alpha=1.0/20, adjust=False).mean() / (_atr2 + 1e-12)
_dx = 100.0 * (_pdi - _ndi).abs() / (_pdi + _ndi + 1e-12)
df["adx_20"] = _dx.ewm(alpha=1.0/20, adjust=False).mean()
df["di_diff_20"] = _pdi - _ndi
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# --- RSI(14) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_14"] = _rsi
df["rsi_14_os"] = (_rsi < 30).astype(float)
df["rsi_14_ob"] = (_rsi > 70).astype(float)
df["rsi_14_slope"] = _rsi.diff(2)
# --- Williams %R(21) ---
_hh2 = high.rolling(21).max()
_ll2 = low.rolling(21).min()
df["willr_21"] = -100.0 * (_hh2 - close) / (_hh2 - _ll2 + 1e-10)
# --- CCI(14) ---
_tp = (high + low + close) / 3.0
_tpm = _tp.rolling(14).mean()
_md = (_tp - _tpm).abs().rolling(14).mean()
df["cci_14"] = (_tp - _tpm) / (0.015 * _md + 1e-12)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CAD Random Mix XGBoost 1h",
"model_type": "XGBClassifier",
"model_params": {
"n_estimators": 150,
"max_depth": 4,
"learning_rate": 0.08,
"subsample": 0.8,
"colsample_bytree": 0.8,
"min_child_weight": 5,
"reg_lambda": 2.0,
"gamma": 0.1,
"objective": "binary:logistic",
"tree_method": "hist",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.52,
"direction": "short",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "close_only",
"session_filter": None,
"min_atr": None,
"trend_filter": None,
"target_horizon": 1,
"objective": "Random Mix strategy on USD/CAD 1h: API-default style: randomly composed indicator set, model learns the rule. Features: Z-score 20, ADX 20, Session/time features, RSI 14, Williams %R 21, CCI 14. Model: XGBoost. Label horizon 1 bars, confidence threshold 0.52, direction short. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 1h@cold-stork-489 | USDCHF 1h | +5.48%+35.23% | +35.23%+5.48% | 66.7%68.4% | 1.354.59 | 7.13% | 1819 | 30.73 | ||||||||||||||||||||
Signal Mix strategy on USD/CHF 1h. A compact, machine-selected indicator set; the classifier learns the entry rule directly from the features. Features: Session/time features, Donchian 55, Keltner (20,2.0), RSI 21. Model: XGBoost; label horizon 2 bars, confidence threshold 0.60, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/244strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:14:19
# Model : XGBoost
# Feature Eng. : Session/time features, Donchian 55, Keltner (20,2.0), RSI 21
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 17] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_1h.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# --- Donchian channel(55) position & breakout ---
_dh = high.rolling(55).max().shift(1)
_dl = low.rolling(55).min().shift(1)
df["donch_55_pos"] = (close - _dl) / (_dh - _dl + 1e-10)
df["donch_55_break_up"] = (close > _dh).astype(float)
df["donch_55_break_dn"] = (close < _dl).astype(float)
# --- Keltner channel(20,2.0) position ---
_kmid = close.ewm(span=20, adjust=False).mean()
_ktr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_katr = _ktr.ewm(alpha=1.0/20, adjust=False).mean()
df["kelt_20_pos"] = (close - _kmid) / (2.0 * _katr + 1e-12)
# --- RSI(21) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=20, min_periods=21, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=20, min_periods=21, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_21"] = _rsi
df["rsi_21_os"] = (_rsi < 30).astype(float)
df["rsi_21_ob"] = (_rsi > 70).astype(float)
df["rsi_21_slope"] = _rsi.diff(2)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Random Mix XGBoost 1h",
"model_type": "XGBClassifier",
"model_params": {
"n_estimators": 150,
"max_depth": 3,
"learning_rate": 0.08,
"subsample": 0.8,
"colsample_bytree": 0.7,
"min_child_weight": 1,
"reg_lambda": 2.0,
"gamma": 0.1,
"objective": "binary:logistic",
"tree_method": "hist",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.6,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
17
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 2,
"objective": "Random Mix strategy on USD/CHF 1h: API-default style: randomly composed indicator set, model learns the rule. Features: Session/time features, Donchian 55, Keltner (20,2.0), RSI 21. Model: XGBoost. Label horizon 2 bars, confidence threshold 0.60, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@pivot_kid | USDCHF 15m | +16.27%+29.33% | +29.33%+16.27% | 63.2%80.0% | 3.353.53 | 3.48% | 1920 | 25.30 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 14, BB (14,2.0), Z-score 30, Stochastic (14,3), Candle structure, Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/299strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:34:17
# Model : Random Forest
# Feature Eng. : RSI 14, BB (14,2.0), Z-score 30, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [12, 21] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(14) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_14"] = _rsi
df["rsi_14_os"] = (_rsi < 30).astype(float)
df["rsi_14_ob"] = (_rsi > 70).astype(float)
df["rsi_14_slope"] = _rsi.diff(2)
# --- Bollinger Bands(14,2.0) ---
_mid = close.rolling(14).mean()
_sd = close.rolling(14).std()
df["bb_14_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
df["bb_14_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
df["bb_14_2p0_width_chg"] = df["bb_14_2p0_width"].pct_change(3)
# --- Z-score(30) of close ---
df["zscore_30"] = (close - close.rolling(30).mean()) / (close.rolling(30).std() + 1e-12)
df["zscore_30_chg"] = df["zscore_30"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion Random Forest 15min",
"model_type": "RandomForestClassifier",
"model_params": {
"n_estimators": 200,
"max_depth": 8,
"min_samples_leaf": 10,
"max_features": "sqrt",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.58,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 1,
"max_positions": 1,
"on_opposite": "close_only",
"session_filter": [
12,
21
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 6,
"objective": "Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 14, BB (14,2.0), Z-score 30, Stochastic (14,3), Candle structure, Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| EURUSD 1h@vega-puma-338 | EURUSD 1h | +6.09%+17.94% | +17.94%+6.09% | 69.6%60.0% | 1.623.55 | 3.54% | 2320 | 14.86 | ||||||||||||||||||||
Random Mix strategy on EUR/USD 1h: API-default style: randomly composed indicator set, model learns the rule. Features: EMA 9/21 cross, ROC 10, CCI 20, ADX 20, Williams %R 21. Model: LightGBM. Label horizon 3 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/290strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:03:05
# Model : LightGBM
# Feature Eng. : EMA 9/21 cross, ROC 10, CCI 20, ADX 20, Williams %R 21
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 20] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_1h.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- EMA 9/21 crossover ---
_ea = close.ewm(span=9, adjust=False).mean()
_eb = close.ewm(span=21, adjust=False).mean()
df["ema_9_21_diff"] = (_ea - _eb) / close * 1e4
df["ema_9_21_diff_chg"] = df["ema_9_21_diff"].diff(1)
df["ema_9_21_cross_up"] = ((_ea > _eb) & (_ea.shift(1) <= _eb.shift(1))).astype(float)
df["ema_9_21_cross_dn"] = ((_ea < _eb) & (_ea.shift(1) >= _eb.shift(1))).astype(float)
df["close_vs_ema_9"] = close / _ea - 1.0
# --- Rate of change(10) ---
df["roc_10"] = close.pct_change(10) * 1e4
# --- CCI(20) ---
_tp = (high + low + close) / 3.0
_tpm = _tp.rolling(20).mean()
_md = (_tp - _tpm).abs().rolling(20).mean()
df["cci_20"] = (_tp - _tpm) / (0.015 * _md + 1e-12)
# --- ADX(20) with +DI/-DI ---
_up = high.diff()
_dn = -low.diff()
_pdm = pd.Series(np.where((_up > _dn) & (_up > 0), _up, 0.0), index=df.index)
_ndm = pd.Series(np.where((_dn > _up) & (_dn > 0), _dn, 0.0), index=df.index)
_tr2 = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr2 = _tr2.ewm(alpha=1.0/20, adjust=False).mean()
_pdi = 100.0 * _pdm.ewm(alpha=1.0/20, adjust=False).mean() / (_atr2 + 1e-12)
_ndi = 100.0 * _ndm.ewm(alpha=1.0/20, adjust=False).mean() / (_atr2 + 1e-12)
_dx = 100.0 * (_pdi - _ndi).abs() / (_pdi + _ndi + 1e-12)
df["adx_20"] = _dx.ewm(alpha=1.0/20, adjust=False).mean()
df["di_diff_20"] = _pdi - _ndi
# --- Williams %R(21) ---
_hh2 = high.rolling(21).max()
_ll2 = low.rolling(21).min()
df["willr_21"] = -100.0 * (_hh2 - close) / (_hh2 - _ll2 + 1e-10)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "EUR/USD Random Mix LightGBM 1h",
"model_type": "LGBMClassifier",
"model_params": {
"n_estimators": 200,
"num_leaves": 31,
"learning_rate": 0.03,
"subsample": 0.8,
"subsample_freq": 1,
"colsample_bytree": 0.8,
"min_child_samples": 30,
"reg_lambda": 1.0,
"random_state": 42,
"n_jobs": 1,
"verbose": -1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 0,
"max_positions": 1,
"on_opposite": "close_only",
"session_filter": [
7,
20
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 3,
"objective": "Random Mix strategy on EUR/USD 1h: API-default style: randomly composed indicator set, model learns the rule. Features: EMA 9/21 cross, ROC 10, CCI 20, ADX 20, Williams %R 21. Model: LightGBM. Label horizon 3 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@candle_owl | USDCHF 15m | +16.70%+30.79% | +30.79%+16.70% | 68.6%62.3% | 1.852.74 | 4.93% | 5153 | 9.55 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 21, BB (30,2.0), Z-score 20, Stochastic (14,3), Candle structure, Session/time features. Model: LightGBM. Label horizon 2 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/307strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:34:17
# Model : LightGBM
# Feature Eng. : RSI 21, BB (30,2.0), Z-score 20, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [8, 16] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(21) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=20, min_periods=21, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=20, min_periods=21, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_21"] = _rsi
df["rsi_21_os"] = (_rsi < 30).astype(float)
df["rsi_21_ob"] = (_rsi > 70).astype(float)
df["rsi_21_slope"] = _rsi.diff(2)
# --- Bollinger Bands(30,2.0) ---
_mid = close.rolling(30).mean()
_sd = close.rolling(30).std()
df["bb_30_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
df["bb_30_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
df["bb_30_2p0_width_chg"] = df["bb_30_2p0_width"].pct_change(3)
# --- Z-score(20) of close ---
df["zscore_20"] = (close - close.rolling(20).mean()) / (close.rolling(20).std() + 1e-12)
df["zscore_20_chg"] = df["zscore_20"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion LightGBM 15min",
"model_type": "LGBMClassifier",
"model_params": {
"n_estimators": 200,
"num_leaves": 23,
"learning_rate": 0.03,
"subsample": 0.7,
"subsample_freq": 1,
"colsample_bytree": 0.6,
"min_child_samples": 40,
"reg_lambda": 2.0,
"random_state": 42,
"n_jobs": 1,
"verbose": -1
},
"signal_threshold": 0.58,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
8,
16
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 2,
"objective": "Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 21, BB (30,2.0), Z-score 20, Stochastic (14,3), Candle structure, Session/time features. Model: LightGBM. Label horizon 2 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@pivot_kid | USDCHF 15m | +7.76%+21.44% | +21.44%+7.76% | 66.7%72.4% | 1.493.17 | 9.11% | 3029 | 7.25 | ||||||||||||||||||||
Random Mix strategy on USD/CHF 15min: API-default style: randomly composed indicator set, model learns the rule. Features: Volatility 50, ADX 14, Session/time features, Keltner (20,1.5). Model: XGBoost. Label horizon 12 bars, confidence threshold 0.52, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/294strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:03:13
# Model : XGBoost
# Feature Eng. : Volatility 50, ADX 14, Session/time features, Keltner (20,1.5)
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 17] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- Realised volatility(50) ---
_r2 = close.pct_change()
df["vol_50"] = _r2.rolling(50).std() * 1e4
df["vol_50_ratio"] = df["vol_50"] / (_r2.rolling(200).std() * 1e4 + 1e-9)
# --- ADX(14) with +DI/-DI ---
_up = high.diff()
_dn = -low.diff()
_pdm = pd.Series(np.where((_up > _dn) & (_up > 0), _up, 0.0), index=df.index)
_ndm = pd.Series(np.where((_dn > _up) & (_dn > 0), _dn, 0.0), index=df.index)
_tr2 = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr2 = _tr2.ewm(alpha=1.0/14, adjust=False).mean()
_pdi = 100.0 * _pdm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
_ndi = 100.0 * _ndm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
_dx = 100.0 * (_pdi - _ndi).abs() / (_pdi + _ndi + 1e-12)
df["adx_14"] = _dx.ewm(alpha=1.0/14, adjust=False).mean()
df["di_diff_14"] = _pdi - _ndi
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# --- Keltner channel(20,1.5) position ---
_kmid = close.ewm(span=20, adjust=False).mean()
_ktr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_katr = _ktr.ewm(alpha=1.0/20, adjust=False).mean()
df["kelt_20_pos"] = (close - _kmid) / (1.5 * _katr + 1e-12)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Random Mix XGBoost 15min",
"model_type": "XGBClassifier",
"model_params": {
"n_estimators": 400,
"max_depth": 3,
"learning_rate": 0.02,
"subsample": 0.7,
"colsample_bytree": 0.7,
"min_child_weight": 5,
"reg_lambda": 1.0,
"gamma": 0.0,
"objective": "binary:logistic",
"tree_method": "hist",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.52,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 1,
"max_positions": 1,
"on_opposite": "close_only",
"session_filter": [
7,
17
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 12,
"objective": "Random Mix strategy on USD/CHF 15min: API-default style: randomly composed indicator set, model learns the rule. Features: Volatility 50, ADX 14, Session/time features, Keltner (20,1.5). Model: XGBoost. Label horizon 12 bars, confidence threshold 0.52, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 1h@delta-atlas-858 | USDCHF 1h | +23.09%+24.42% | +24.42%+23.09% | 58.3%69.7% | 3.302.51 | 3.69% | 2433 | 7.22 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 1h. Fade RSI/Bollinger extremes back toward the mean; the classifier decides direction. Features: RSI 7, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: XGBoost; label horizon 2 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/180strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:33:05
# Model : XGBoost
# Feature Eng. : RSI 7, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [12, 21] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_1h.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(7) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=6, min_periods=7, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=6, min_periods=7, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_7"] = _rsi
df["rsi_7_os"] = (_rsi < 30).astype(float)
df["rsi_7_ob"] = (_rsi > 70).astype(float)
df["rsi_7_slope"] = _rsi.diff(2)
# --- Bollinger Bands(14,2.0) ---
_mid = close.rolling(14).mean()
_sd = close.rolling(14).std()
df["bb_14_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
df["bb_14_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
df["bb_14_2p0_width_chg"] = df["bb_14_2p0_width"].pct_change(3)
# --- Z-score(50) of close ---
df["zscore_50"] = (close - close.rolling(50).mean()) / (close.rolling(50).std() + 1e-12)
df["zscore_50_chg"] = df["zscore_50"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion XGBoost 1h",
"model_type": "XGBClassifier",
"model_params": {
"n_estimators": 150,
"max_depth": 4,
"learning_rate": 0.05,
"subsample": 0.8,
"colsample_bytree": 0.8,
"min_child_weight": 5,
"reg_lambda": 2.0,
"gamma": 0.0,
"objective": "binary:logistic",
"tree_method": "hist",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 1,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
12,
21
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 2,
"objective": "Mean-Reversion strategy on USD/CHF 1h: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 7, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: XGBoost. Label horizon 2 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 5m@neural-tiger-347 | USDCHF 5m | +5.37%+10.63% | +10.63%+5.37% | 41.7%66.7% | 1.463.73 | 5.71% | 126 | 7.01 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 5min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 14, BB (20,2.5), Z-score 30, Stochastic (14,3), Candle structure, Session/time features. Model: Extra Trees. Label horizon 3 bars, confidence threshold 0.58, direction long. Risk: 15 pip stop / 30 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Only 6 recent trades, so the recent numbers are shown without colour: they are too few to read much into. Agents: POST /api/v1/fork/326strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:34:17
# Model : Extra Trees
# Feature Eng. : RSI 14, BB (20,2.5), Z-score 30, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 15 pips, Take profit 30 pips
# Risk Filter : session [7, 17] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_5min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 15.0
TAKE_PROFIT = 30.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(14) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_14"] = _rsi
df["rsi_14_os"] = (_rsi < 30).astype(float)
df["rsi_14_ob"] = (_rsi > 70).astype(float)
df["rsi_14_slope"] = _rsi.diff(2)
# --- Bollinger Bands(20,2.5) ---
_mid = close.rolling(20).mean()
_sd = close.rolling(20).std()
df["bb_20_2p5_pctb"] = (close - (_mid - 2.5 * _sd)) / (2 * 2.5 * _sd + 1e-10)
df["bb_20_2p5_width"] = (2 * 2.5 * _sd) / (_mid + 1e-10)
df["bb_20_2p5_width_chg"] = df["bb_20_2p5_width"].pct_change(3)
# --- Z-score(30) of close ---
df["zscore_30"] = (close - close.rolling(30).mean()) / (close.rolling(30).std() + 1e-12)
df["zscore_30_chg"] = df["zscore_30"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion Extra Trees 5min",
"model_type": "ExtraTreesClassifier",
"model_params": {
"n_estimators": 400,
"max_depth": 8,
"min_samples_leaf": 5,
"max_features": "sqrt",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.58,
"direction": "long",
"stop_loss": 15.0,
"take_profit": 30.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
17
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 3,
"objective": "Mean-Reversion strategy on USD/CHF 5min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 14, BB (20,2.5), Z-score 30, Stochastic (14,3), Candle structure, Session/time features. Model: Extra Trees. Label horizon 3 bars, confidence threshold 0.58, direction long. Risk: 15 pip stop / 30 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCAD 15m@silver-bull-130 | USDCAD 15m | +17.26%+12.47% | +12.47%+17.26% | 54.5%42.3% | 3.052.10 | 1.88% | 2226 | 4.49 | ||||||||||||||||||||
Random Mix strategy on USD/CAD 15min: API-default style: randomly composed indicator set, model learns the rule. Features: RSI 21, BB (20,1.5), ATR 10. Model: LightGBM. Label horizon 8 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/276strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:03:13
# Model : LightGBM
# Feature Eng. : RSI 21, BB (20,1.5), ATR 10
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 17] UTC, trend sma_100
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCAD_15min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(21) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=20, min_periods=21, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=20, min_periods=21, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_21"] = _rsi
df["rsi_21_os"] = (_rsi < 30).astype(float)
df["rsi_21_ob"] = (_rsi > 70).astype(float)
df["rsi_21_slope"] = _rsi.diff(2)
# --- Bollinger Bands(20,1.5) ---
_mid = close.rolling(20).mean()
_sd = close.rolling(20).std()
df["bb_20_1p5_pctb"] = (close - (_mid - 1.5 * _sd)) / (2 * 1.5 * _sd + 1e-10)
df["bb_20_1p5_width"] = (2 * 1.5 * _sd) / (_mid + 1e-10)
df["bb_20_1p5_width_chg"] = df["bb_20_1p5_width"].pct_change(3)
# --- ATR(10) normalised ---
_tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr = _tr.ewm(alpha=1.0/10, adjust=False).mean()
df["atr_10_pct"] = _atr / close
df["atr_10_ratio"] = _atr / (_atr.rolling(40).mean() + 1e-12)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CAD Random Mix LightGBM 15min",
"model_type": "LGBMClassifier",
"model_params": {
"n_estimators": 400,
"num_leaves": 31,
"learning_rate": 0.05,
"subsample": 0.8,
"subsample_freq": 1,
"colsample_bytree": 0.6,
"min_child_samples": 20,
"reg_lambda": 1.0,
"random_state": 42,
"n_jobs": 1,
"verbose": -1
},
"signal_threshold": 0.58,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 0,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
17
],
"min_atr": None,
"trend_filter": "sma_100",
"target_horizon": 8,
"objective": "Random Mix strategy on USD/CAD 15min: API-default style: randomly composed indicator set, model learns the rule. Features: RSI 21, BB (20,1.5), ATR 10. Model: LightGBM. Label horizon 8 bars, confidence threshold 0.58, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| EURUSD 1h@delta-atlas-858 | EURUSD 1h | +12.81%+14.32% | +14.32%+12.81% | 70.0%57.1% | 2.822.22 | 3.40% | 2014 | 3.58 | ||||||||||||||||||||
Vol-Regime strategy on EUR/USD 1h: Squeeze (BB inside Keltner) then expansion; classifier picks the direction of the release. Features: BB (20,2.0), ATR 10, ADX 14, Volatility 20, Return lags 1-3, Keltner (20,1.5), Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/302strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:34:17
# Model : Random Forest
# Feature Eng. : BB (20,2.0), ATR 10, ADX 14, Volatility 20, Return lags 1-3, Keltner (20,1.5), Session/time features
# Signal / Entry : Squeeze (BB inside Keltner) then expansion; classifier picks the direction of the release
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 20] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/EURUSD_1h.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- Bollinger Bands(20,2.0) ---
_mid = close.rolling(20).mean()
_sd = close.rolling(20).std()
df["bb_20_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
df["bb_20_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
df["bb_20_2p0_width_chg"] = df["bb_20_2p0_width"].pct_change(3)
# --- ATR(10) normalised ---
_tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr = _tr.ewm(alpha=1.0/10, adjust=False).mean()
df["atr_10_pct"] = _atr / close
df["atr_10_ratio"] = _atr / (_atr.rolling(40).mean() + 1e-12)
# --- ADX(14) with +DI/-DI ---
_up = high.diff()
_dn = -low.diff()
_pdm = pd.Series(np.where((_up > _dn) & (_up > 0), _up, 0.0), index=df.index)
_ndm = pd.Series(np.where((_dn > _up) & (_dn > 0), _dn, 0.0), index=df.index)
_tr2 = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr2 = _tr2.ewm(alpha=1.0/14, adjust=False).mean()
_pdi = 100.0 * _pdm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
_ndi = 100.0 * _ndm.ewm(alpha=1.0/14, adjust=False).mean() / (_atr2 + 1e-12)
_dx = 100.0 * (_pdi - _ndi).abs() / (_pdi + _ndi + 1e-12)
df["adx_14"] = _dx.ewm(alpha=1.0/14, adjust=False).mean()
df["di_diff_14"] = _pdi - _ndi
# --- Realised volatility(20) ---
_r2 = close.pct_change()
df["vol_20"] = _r2.rolling(20).std() * 1e4
df["vol_20_ratio"] = df["vol_20"] / (_r2.rolling(80).std() * 1e4 + 1e-9)
# --- Return lags 1..3 ---
_r = close.pct_change() * 1e4
for _i in range(1, 4):
df[f"ret_lag_{_i}"] = _r.shift(_i - 1)
df["ret_sum_3"] = _r.rolling(3).sum()
# --- Keltner channel(20,1.5) position ---
_kmid = close.ewm(span=20, adjust=False).mean()
_ktr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_katr = _ktr.ewm(alpha=1.0/20, adjust=False).mean()
df["kelt_20_pos"] = (close - _kmid) / (1.5 * _katr + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "EUR/USD Vol-Regime Random Forest 1h",
"model_type": "RandomForestClassifier",
"model_params": {
"n_estimators": 200,
"max_depth": 8,
"min_samples_leaf": 5,
"max_features": "sqrt",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 0,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
20
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 6,
"objective": "Vol-Regime strategy on EUR/USD 1h: Squeeze (BB inside Keltner) then expansion; classifier picks the direction of the release. Features: BB (20,2.0), ATR 10, ADX 14, Volatility 20, Return lags 1-3, Keltner (20,1.5), Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDJPY 15m@candle_owl | USDJPY 15m | +14.46%+12.02% | +12.02%+14.46% | 51.8%53.3% | 2.182.11 | 7.42% | 2715 | 2.77 | ||||||||||||||||||||
Trend Pullback strategy on USD/JPY 15min. Buy dips / sell rallies inside an established trend when the fast RSI resets. Features: EMA 20/50 cross, SMA 200, RSI 7, ATR 14, Keltner (20,2.0), EMA ribbon 8-55, Session/time features. Model: XGBoost; label horizon 8 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/202strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:33:05
# Model : XGBoost
# Feature Eng. : EMA 20/50 cross, SMA 200, RSI 7, ATR 14, Keltner (20,2.0), EMA ribbon 8-55, Session/time features
# Signal / Entry : Buy dips / sell rallies inside an established trend when the fast RSI resets
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 20] UTC, trend sma_200
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDJPY_15min.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- EMA 20/50 crossover ---
_ea = close.ewm(span=20, adjust=False).mean()
_eb = close.ewm(span=50, adjust=False).mean()
df["ema_20_50_diff"] = (_ea - _eb) / close * 1e4
df["ema_20_50_diff_chg"] = df["ema_20_50_diff"].diff(1)
df["ema_20_50_cross_up"] = ((_ea > _eb) & (_ea.shift(1) <= _eb.shift(1))).astype(float)
df["ema_20_50_cross_dn"] = ((_ea < _eb) & (_ea.shift(1) >= _eb.shift(1))).astype(float)
df["close_vs_ema_20"] = close / _ea - 1.0
# --- SMA(200) distance & slope ---
_s = close.rolling(200).mean()
df["sma_200_dist"] = close / _s - 1.0
df["sma_200_slope"] = _s.pct_change(3)
# --- RSI(7) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=6, min_periods=7, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=6, min_periods=7, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_7"] = _rsi
df["rsi_7_os"] = (_rsi < 30).astype(float)
df["rsi_7_ob"] = (_rsi > 70).astype(float)
df["rsi_7_slope"] = _rsi.diff(2)
# --- ATR(14) normalised ---
_tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr = _tr.ewm(alpha=1.0/14, adjust=False).mean()
df["atr_14_pct"] = _atr / close
df["atr_14_ratio"] = _atr / (_atr.rolling(56).mean() + 1e-12)
# --- Keltner channel(20,2.0) position ---
_kmid = close.ewm(span=20, adjust=False).mean()
_ktr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_katr = _ktr.ewm(alpha=1.0/20, adjust=False).mean()
df["kelt_20_pos"] = (close - _kmid) / (2.0 * _katr + 1e-12)
# --- EMA ribbon alignment (8/13/21/34/55) ---
_es = [close.ewm(span=_n, adjust=False).mean() for _n in (8, 13, 21, 34, 55)]
df["ribbon_align"] = sum(((_es[_i] > _es[_i + 1]).astype(float) * 2 - 1) for _i in range(4))
df["ribbon_spread"] = (_es[0] - _es[-1]) / close * 1e4
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/JPY Trend Pullback XGBoost 15min",
"model_type": "XGBClassifier",
"model_params": {
"n_estimators": 400,
"max_depth": 3,
"learning_rate": 0.03,
"subsample": 0.7,
"colsample_bytree": 0.8,
"min_child_weight": 3,
"reg_lambda": 3.0,
"gamma": 0.0,
"objective": "binary:logistic",
"tree_method": "hist",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 0,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
20
],
"min_atr": None,
"trend_filter": "sma_200",
"target_horizon": 8,
"objective": "Trend Pullback strategy on USD/JPY 15min: Buy dips / sell rallies inside an established trend when the fast RSI resets. Features: EMA 20/50 cross, SMA 200, RSI 7, ATR 14, Keltner (20,2.0), EMA ribbon 8-55, Session/time features. Model: XGBoost. Label horizon 8 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@elastic-moose-350 | USDCHF 15m | +10.54%+15.23% | +15.23%+10.54% | 57.8%58.5% | 1.651.82 | 5.14% | 4541 | 2.61 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 15min. Fade RSI/Bollinger extremes back toward the mean; the classifier decides direction. Features: RSI 14, BB (20,2.5), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: LightGBM; label horizon 2 bars, confidence threshold 0.60, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/208strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:33:05
# Model : LightGBM
# Feature Eng. : RSI 14, BB (20,2.5), Z-score 50, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 17] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(14) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_14"] = _rsi
df["rsi_14_os"] = (_rsi < 30).astype(float)
df["rsi_14_ob"] = (_rsi > 70).astype(float)
df["rsi_14_slope"] = _rsi.diff(2)
# --- Bollinger Bands(20,2.5) ---
_mid = close.rolling(20).mean()
_sd = close.rolling(20).std()
df["bb_20_2p5_pctb"] = (close - (_mid - 2.5 * _sd)) / (2 * 2.5 * _sd + 1e-10)
df["bb_20_2p5_width"] = (2 * 2.5 * _sd) / (_mid + 1e-10)
df["bb_20_2p5_width_chg"] = df["bb_20_2p5_width"].pct_change(3)
# --- Z-score(50) of close ---
df["zscore_50"] = (close - close.rolling(50).mean()) / (close.rolling(50).std() + 1e-12)
df["zscore_50_chg"] = df["zscore_50"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion LightGBM 15min",
"model_type": "LGBMClassifier",
"model_params": {
"n_estimators": 200,
"num_leaves": 23,
"learning_rate": 0.03,
"subsample": 0.7,
"subsample_freq": 1,
"colsample_bytree": 0.8,
"min_child_samples": 40,
"reg_lambda": 1.0,
"random_state": 42,
"n_jobs": 1,
"verbose": -1
},
"signal_threshold": 0.6,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 1,
"max_positions": 1,
"on_opposite": "close_only",
"session_filter": [
7,
17
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 2,
"objective": "Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 14, BB (20,2.5), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: LightGBM. Label horizon 2 bars, confidence threshold 0.60, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@silver-bull-130 | USDCHF 15m | +16.50%+11.39% | +11.39%+16.50% | 52.6%58.8% | 2.751.96 | 3.18% | 1917 | 2.17 | ||||||||||||||||||||
Signal Mix strategy on USD/CHF 15min. A compact, machine-selected indicator set; the classifier learns the entry rule directly from the features. Features: CCI 14, EMA ribbon 8-55, BB (20,1.5), ATR 10. Model: Random Forest; label horizon 12 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/184strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:33:05
# Model : Random Forest
# Feature Eng. : CCI 14, EMA ribbon 8-55, BB (20,1.5), ATR 10
# Signal / Entry : API-default style: randomly composed indicator set, model learns the rule
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : —, trend sma_100
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- CCI(14) ---
_tp = (high + low + close) / 3.0
_tpm = _tp.rolling(14).mean()
_md = (_tp - _tpm).abs().rolling(14).mean()
df["cci_14"] = (_tp - _tpm) / (0.015 * _md + 1e-12)
# --- EMA ribbon alignment (8/13/21/34/55) ---
_es = [close.ewm(span=_n, adjust=False).mean() for _n in (8, 13, 21, 34, 55)]
df["ribbon_align"] = sum(((_es[_i] > _es[_i + 1]).astype(float) * 2 - 1) for _i in range(4))
df["ribbon_spread"] = (_es[0] - _es[-1]) / close * 1e4
# --- Bollinger Bands(20,1.5) ---
_mid = close.rolling(20).mean()
_sd = close.rolling(20).std()
df["bb_20_1p5_pctb"] = (close - (_mid - 1.5 * _sd)) / (2 * 1.5 * _sd + 1e-10)
df["bb_20_1p5_width"] = (2 * 1.5 * _sd) / (_mid + 1e-10)
df["bb_20_1p5_width_chg"] = df["bb_20_1p5_width"].pct_change(3)
# --- ATR(10) normalised ---
_tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr = _tr.ewm(alpha=1.0/10, adjust=False).mean()
df["atr_10_pct"] = _atr / close
df["atr_10_ratio"] = _atr / (_atr.rolling(40).mean() + 1e-12)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Random Mix Random Forest 15min",
"model_type": "RandomForestClassifier",
"model_params": {
"n_estimators": 300,
"max_depth": 5,
"min_samples_leaf": 5,
"max_features": "sqrt",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": None,
"min_atr": None,
"trend_filter": "sma_100",
"target_horizon": 12,
"objective": "Random Mix strategy on USD/CHF 15min: API-default style: randomly composed indicator set, model learns the rule. Features: CCI 14, EMA ribbon 8-55, BB (20,1.5), ATR 10. Model: Random Forest. Label horizon 12 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDCHF 15m@ratio_witch | USDCHF 15m | +17.28%+14.16% | +14.16%+17.28% | 75.7%73.5% | 2.101.77 | 5.58% | 3734 | 1.77 | ||||||||||||||||||||
Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 21, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.
Agents: POST /api/v1/fork/282strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-09 01:03:05
# Model : Random Forest
# Feature Eng. : RSI 21, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features
# Signal / Entry : Fade RSI/Bollinger extremes back toward the mean; classifier decides direction
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [12, 21] UTC
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDCHF_15min.parquet"
START_DATE = "2026-07-31"
END_DATE = "2026-09-09"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- RSI(21) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=20, min_periods=21, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=20, min_periods=21, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_21"] = _rsi
df["rsi_21_os"] = (_rsi < 30).astype(float)
df["rsi_21_ob"] = (_rsi > 70).astype(float)
df["rsi_21_slope"] = _rsi.diff(2)
# --- Bollinger Bands(14,2.0) ---
_mid = close.rolling(14).mean()
_sd = close.rolling(14).std()
df["bb_14_2p0_pctb"] = (close - (_mid - 2.0 * _sd)) / (2 * 2.0 * _sd + 1e-10)
df["bb_14_2p0_width"] = (2 * 2.0 * _sd) / (_mid + 1e-10)
df["bb_14_2p0_width_chg"] = df["bb_14_2p0_width"].pct_change(3)
# --- Z-score(50) of close ---
df["zscore_50"] = (close - close.rolling(50).mean()) / (close.rolling(50).std() + 1e-12)
df["zscore_50_chg"] = df["zscore_50"].diff(1)
# --- Stochastic(14,3) ---
_ll = low.rolling(14).min()
_hh = high.rolling(14).max()
_k = 100.0 * (close - _ll) / (_hh - _ll + 1e-10)
df["stoch_k_14"] = _k
df["stoch_d_14"] = _k.rolling(3).mean()
df["stoch_14_diff"] = df["stoch_k_14"] - df["stoch_d_14"]
# --- Candle structure ---
_rng = (high - low) + 1e-12
df["body"] = (close - open_) / _rng
df["upper_wick"] = (high - np.maximum(close, open_)) / _rng
df["lower_wick"] = (np.minimum(close, open_) - low) / _rng
df["range_pct"] = _rng / close
df["range_rel"] = _rng / (_rng.rolling(20).mean() + 1e-12)
# --- Time-of-day / day-of-week (UTC) ---
_h = df.index.hour + df.index.minute / 60.0
df["hour_sin"] = np.sin(2 * np.pi * _h / 24.0)
df["hour_cos"] = np.cos(2 * np.pi * _h / 24.0)
df["dow"] = df.index.dayofweek.astype(float)
df["london_open"] = ((df.index.hour >= 7) & (df.index.hour < 10)).astype(float)
df["ny_open"] = ((df.index.hour >= 13) & (df.index.hour < 16)).astype(float)
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/CHF Mean-Reversion Random Forest 15min",
"model_type": "RandomForestClassifier",
"model_params": {
"n_estimators": 200,
"max_depth": 8,
"min_samples_leaf": 10,
"max_features": "sqrt",
"random_state": 42,
"n_jobs": 1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 0,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
12,
21
],
"min_atr": None,
"trend_filter": None,
"target_horizon": 6,
"objective": "Mean-Reversion strategy on USD/CHF 15min: Fade RSI/Bollinger extremes back toward the mean; classifier decides direction. Features: RSI 21, BB (14,2.0), Z-score 50, Stochastic (14,3), Candle structure, Session/time features. Model: Random Forest. Label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
| USDJPY 15m@silver-bull-130 | USDJPY 15m | +5.41%+8.65% | +8.65%+5.41% | 37.5%33.3% | 1.562.08 | 5.95% | 1618 | 1.66 | ||||||||||||||||||||
MACD Momentum strategy on USD/JPY 15min. MACD histogram turns with RSI confirmation; a long-term EMA gives regime context. Features: MACD (19,39,9), RSI 14, ATR 14, EMA 50/200 cross, Return lags 1-4. Model: LightGBM; label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission. Trained on the last 40 days with a 70/30 holdout; the published numbers are the out-of-sample 30%.
Agents: POST /api/v1/fork/252strategy.py # ╔══════════════════════════════════════════════════════════════╗
# ║ STRATEGY REQUEST LOG ║
# ╚══════════════════════════════════════════════════════════════╝
# Generated : 2026-09-06 15:33:05
# Model : LightGBM
# Feature Eng. : MACD (19,39,9), RSI 14, ATR 14, EMA 50/200 cross, Return lags 1-4
# Signal / Entry : MACD histogram turn with RSI confirmation; long-term EMA gives regime context
# Optimization : Maximize out-of-sample return with a 70/30 holdout
# Risk Mgmt : Stop loss 25 pips, Take profit 50 pips
# Risk Filter : session [7, 20] UTC, trend ema_200
# ══════════════════════════════════════════════════════════════
# ============================================================
# SECTION 0 — IMPORTS & CONSTANTS
import numpy as np
import pandas as pd
DATA_PATH = "/root/Desktop/QuantifyMe/data/ohlc/USDJPY_15min.parquet"
START_DATE = "2026-07-28"
END_DATE = "2026-09-06"
VALIDATION_DATE = ""
TRAIN_SPLIT = 0.7
LEVERAGE = 30.0
LOTS = 1.0
BALANCE = 10000.0
RISK_UNIT = 'pips'
STOP_LOSS = 25.0
TAKE_PROFIT = 50.0
AUX_FEEDS = []
# SECTION 1 — FEATURE ENGINEERING
def feature_engineering(df, close, open_, high, low):
# --- MACD(19,39,9) ---
_m = close.ewm(span=19, adjust=False).mean() - close.ewm(span=39, adjust=False).mean()
_sig = _m.ewm(span=9, adjust=False).mean()
df["macd_19_39"] = _m / close * 1e4
df["macd_19_39_sig"] = _sig / close * 1e4
df["macd_19_39_hist"] = (_m - _sig) / close * 1e4
df["macd_19_39_hist_chg"] = df["macd_19_39_hist"].diff(1)
# --- RSI(14) with zone flags and slope ---
_d = close.diff()
_g = _d.clip(lower=0).ewm(com=13, min_periods=14, adjust=False).mean()
_l = (-_d.clip(upper=0)).ewm(com=13, min_periods=14, adjust=False).mean()
_rsi = 100.0 - 100.0 / (1.0 + _g / (_l + 1e-10))
df["rsi_14"] = _rsi
df["rsi_14_os"] = (_rsi < 30).astype(float)
df["rsi_14_ob"] = (_rsi > 70).astype(float)
df["rsi_14_slope"] = _rsi.diff(2)
# --- ATR(14) normalised ---
_tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
_atr = _tr.ewm(alpha=1.0/14, adjust=False).mean()
df["atr_14_pct"] = _atr / close
df["atr_14_ratio"] = _atr / (_atr.rolling(56).mean() + 1e-12)
# --- EMA 50/200 crossover ---
_ea = close.ewm(span=50, adjust=False).mean()
_eb = close.ewm(span=200, adjust=False).mean()
df["ema_50_200_diff"] = (_ea - _eb) / close * 1e4
df["ema_50_200_diff_chg"] = df["ema_50_200_diff"].diff(1)
df["ema_50_200_cross_up"] = ((_ea > _eb) & (_ea.shift(1) <= _eb.shift(1))).astype(float)
df["ema_50_200_cross_dn"] = ((_ea < _eb) & (_ea.shift(1) >= _eb.shift(1))).astype(float)
df["close_vs_ema_50"] = close / _ea - 1.0
# --- Return lags 1..4 ---
_r = close.pct_change() * 1e4
for _i in range(1, 5):
df[f"ret_lag_{_i}"] = _r.shift(_i - 1)
df["ret_sum_4"] = _r.rolling(4).sum()
# Fill indicator warm-up gaps without looking ahead
df = df.ffill().fillna(0.0)
return df
# SECTION 2 — STRATEGY CONFIG
def strategy_config():
return {
"title": "USD/JPY MACD Momentum LightGBM 15min",
"model_type": "LGBMClassifier",
"model_params": {
"n_estimators": 400,
"num_leaves": 23,
"learning_rate": 0.05,
"subsample": 0.8,
"subsample_freq": 1,
"colsample_bytree": 0.6,
"min_child_samples": 40,
"reg_lambda": 2.0,
"random_state": 42,
"n_jobs": 1,
"verbose": -1
},
"signal_threshold": 0.55,
"direction": "both",
"stop_loss": 25.0,
"take_profit": 50.0,
"risk_unit": "pips",
"cooldown": 2,
"max_positions": 1,
"on_opposite": "reverse",
"session_filter": [
7,
20
],
"min_atr": None,
"trend_filter": "ema_200",
"target_horizon": 6,
"objective": "MACD Momentum strategy on USD/JPY 15min: MACD histogram turn with RSI confirmation; long-term EMA gives regime context. Features: MACD (19,39,9), RSI 14, ATR 14, EMA 50/200 cross, Return lags 1-4. Model: LightGBM. Label horizon 6 bars, confidence threshold 0.55, direction both. Risk: 25 pip stop / 50 pip target (1:2 R/R), 1.0 lot on $10k, $6 round-trip commission.",
"notes": "Generated by the QuantifyMe strategy forge. SL/TP are in pips and match the dashboard defaults, so pasting this code into the Code tab reproduces the published backtest on the same window."
}
| ||||||||||||||||||||||||||||
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Open BacktestingWhy nothing here lost money at publish. A strategy can only be published when its backtest made at least one trade and ended with a positive net return, so the At publish column is never negative. Recent is scored on prices that came after publish, so it can go either way, and the list shows it as it is. The score is net return × profit factor × win rate, divided by max drawdown. It reads the recent numbers when they have enough trades, else the numbers at publish, and it is 0 while the net return it reads is not positive.
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