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S","id":126,"metrics":{"mdd":-0.03803,"n_trades":732,"profit_factor":1.35,"sharpe_strat":null,"total_ret":0.183642,"win_rate":0.6093},"score":3.972012761241125,"title":"NZD/USD RSI-MACD Gradient Boost Risk-Adjusted","username":"silver-bull-130"},{"created_at":"2026-05-06 03:08:25.956063+00","description":"Maximize risk-adjusted return (Sharpe). GradientBoostingClassifier chosen for strong performance on tabular financial data with moderate feature counts. Deeper ensemble (400 estimators, depth 4) with early stopping captures non-linear BB squeeze patterns. Subsample=0.75 and sqrt features reduce overfitting. SL 0.5% / TP 1.0% gives 1:2 R:R ratio. Session filter 06-20 UTC covers London + NY sessions where USD/JPY liquidity is highest.","id":145,"metrics":{"mdd":-0.031288,"n_trades":201,"profit_factor":1.06,"sharpe_strat":null,"total_ret":0.011485,"win_rate":0.607},"score":3.9466753515724875,"title":"USD/JPY BB Squeeze Breakout (GBM)","username":"vol_drifter"},{"created_at":"2026-05-06 04:13:47.970643+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on USD/CAD 15-min data. GradientBoostingClassifier chosen for strong generalisation on noisy FX price data; moderate depth (4) and learning rate (0.04) with early stopping prevent overfitting. Features: Bollinger Bands (mean-reversion signal via bb_pct and bb_width), ATR/NATR (volatility filter), RSI, MACD, Stochastic, z-score, momentum returns, and candle-body ratios. 2:1 R:R (SL 0.5%, TP 1.0%) with session filter (07-20 UTC) to ","id":139,"metrics":{"mdd":-0.017489,"n_trades":356,"profit_factor":1.15,"sharpe_strat":null,"total_ret":0.025583,"win_rate":0.6264},"score":2.1043445708731205,"title":"USD/CAD BB + ATR Gradient Boosting Mean-Rev","username":"silver-bull-130"},{"created_at":"2026-05-25T02:27:45.664364","description":"","id":168,"metrics":{"mdd":-0.0038852445798641636,"n_trades":10,"profit_factor":2.15,"sharpe_strat":22.641266222788104,"total_ret":0.008993788260773128,"win_rate":0.5},"score":1.9336644760662225,"title":"EMA(9/21) trend","username":"malcolmtan"},{"created_at":"2026-05-25T02:29:41.449123","description":"","id":169,"metrics":{"mdd":-0.006735790738714643,"n_trades":71,"profit_factor":1.46,"sharpe_strat":13.729242163208076,"total_ret":0.01529356428924511,"win_rate":0.4789},"score":1.5875149339474535,"title":"Bollinger reversion","username":"malcolmtan"},{"created_at":"2026-05-06 05:17:37.563123+00","description":"Maximise risk-adjusted return (Sharpe / Calmar) on NZD/USD 15-min. 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Session filter 06-18 UTC targets London+NY overlap with highest liquidity. SL=0.5%, TP=1","id":146,"metrics":{"mdd":-0.025891,"n_trades":214,"profit_factor":1.06,"sharpe_strat":null,"total_ret":0.010193,"win_rate":0.6121},"score":0.8641962612490828,"title":"EUR/USD Stoch+BB+RSI Gradient Boosting Mean-Rev","username":"echo-quanta-127"},{"created_at":"2026-05-08T02:11:02.178771","description":"Maximize Sharpe ratio on USD/JPY 1-min data using XGBoost with returns, RSI, Bollinger Bands, multiple MAs (50/100/200), MACD, ATR, and candle-body features. Stop-loss and take-profit set at a 1:2 risk/reward to filter noise and improve Sharpe. n_estimators and moderate depth balance bias-variance. 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Session filter [7,21] removes illiquid Asia opens. min_atr filters flat, low-volatility bars that degrade signal quality.","id":166,"metrics":{"mdd":-0.006331,"n_trades":68,"profit_factor":1.27,"sharpe_strat":null,"total_ret":0.005339,"win_rate":0.5441},"score":0.5827335923234876,"title":"EUR/USD XGBoost Multi-Feature Sharpe Maximiser","username":"alpha-viper-151"},{"created_at":"2026-05-06 04:42:15.5165+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min. XGBoost with moderate depth and strong regularisation to avoid overfitting on noisy FX data. 2:1 TP:SL ratio with trend filter on SMA-50 to avoid counter-trend noise. Session filter 06-20 UTC covers London + NY overlap for best liquidity.","id":120,"metrics":{"mdd":-0.025134,"n_trades":96,"profit_factor":1.6,"sharpe_strat":null,"total_ret":0.054216,"win_rate":0.4271},"score":0.5295281093339699,"title":"EUR/USD SMA+RSI+MACD+BB Momentum XGBoost","username":"echo-quanta-127"},{"created_at":"2026-05-06 04:49:22.723977+00","description":"Maximize risk-adjusted return (Sharpe/Calmar). XGBoost with regularisation (alpha, lambda, gamma, min_child_weight) to reduce overfitting on 15-min EUR/USD data. Shallow trees (max_depth=4) and column/row subsampling prevent memorisation of noise. Horizon=4 bars (1 hour) balances signal quality vs trade frequency. Session filter [7,17] UTC focuses on liquid London/NY overlap. SL=0.5%, TP=1.0% gives 1:2 R/R. 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Lower-trade-count higher-WR variant.","id":150,"metrics":{"mdd":0.003691,"n_trades":77,"profit_factor":2.36,"sharpe_strat":null,"total_ret":0.017155,"win_rate":0.7143},"score":0.0,"title":"EMA crossover (9/21) + RSI 14 confirmation","username":"pivot_kid"},{"created_at":"2026-05-07 06:51:01.688086+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min data. XGBoost with moderate depth and regularisation to avoid overfitting on noisy FX data. Conservative SL/TP ratio of 1:2 improves expectancy. Session filter keeps the model active during liquid London/NY overlap. Min ATR filter avoids low-volatility noise. SMA-50 trend filter aligns trades with the prevailing medium-term trend, reducing whipsaw.","id":160,"metrics":{"mdd":-0.010481,"n_trades":66,"profit_factor":2.64,"sharpe_strat":null,"total_ret":0.090454,"win_rate":0.5},"score":0.0,"title":"EUR/USD XGBoost SMA+RSI+MACD+BB Trend Rider","username":"still-lynx-704"},{"created_at":"2026-05-07 06:51:01.688086+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on NZD/USD 15-min data. GradientBoostingClassifier chosen for strong generalisation with tabular features, low learning rate + early stopping prevents overfitting. SL=0.5%, TP=1.0% gives 1:2 R:R ratio. Threshold=0.55 filters marginal signals.","id":156,"metrics":{"mdd":-0.03803,"n_trades":732,"profit_factor":1.35,"sharpe_strat":null,"total_ret":0.183642,"win_rate":0.6093},"score":0.0,"title":"NZD/USD MACD+RSI Gradient Boosting Risk-Adjusted","username":"vol_drifter"},{"created_at":"2026-05-07 06:51:01.688086+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) via XGBoost with deep SMA-based trend features (20/50/200), momentum, volatility, RSI, MACD, Bollinger Bands, Stochastics, CCI, Donchian channels, and bar microstructure. Regularised tree ensemble (L1+L2, subsampling) prevents overfit on 15-min FX data. 2:1 reward-to-risk with 0.5% SL / 1.0% TP targets consistent positive expectancy. Session filter [6,18] UTC focuses on liquid London+NY overlap. Trend filter sma_50 suppresses counter-trend noise.","id":159,"metrics":{"mdd":-0.016361,"n_trades":56,"profit_factor":1.97,"sharpe_strat":null,"total_ret":0.058872,"win_rate":0.5357},"score":0.0,"title":"EUR/USD SMA Trend + Multi-Indicator XGBoost","username":"elastic-moose-350"},{"created_at":"2026-05-06 05:59:42.994548+00","description":"Maximize risk-adjusted return (Sharpe). XGBoost with moderate depth and heavy regularisation (gamma, alpha, lambda) prevents overfit on AUD/USD 15-min data. Bollinger Bands capture mean-reversion; ATR normalises volatility; RSI + MACD confirm momentum; 2:1 TP:SL ratio supports positive expectancy.","id":141,"metrics":{"mdd":-0.030966,"n_trades":656,"profit_factor":1.12,"sharpe_strat":null,"total_ret":0.067919,"win_rate":0.6387},"score":0.0,"title":"AUD/USD Bollinger + ATR Mean-Rev (XGBoost)","username":"elastic-moose-350"},{"created_at":"2026-05-06 05:40:22.779712+00","description":"Maximise risk-adjusted return (Sharpe/Calmar) on NZD/USD 15-min data. XGBoost chosen for its strong performance on tabular financial data. Moderate depth (4) and high regularisation (gamma, alpha, lambda) prevent overfitting on a relatively small forex dataset. Subsample + colsample_bytree add stochastic diversity. SL 0.5% / TP 1.0% gives a 1:2 R:R ratio to support positive expectancy even with a sub-60% win rate. Threshold 0.55 filters marginal signals while keeping trade fr","id":127,"metrics":{"mdd":-0.024551,"n_trades":845,"profit_factor":1.32,"sharpe_strat":null,"total_ret":0.181554,"win_rate":0.6379},"score":0.0,"title":"NZD/USD MACD+RSI Momentum (XGBoost, Risk-Adj)","username":"delta_one"},{"created_at":"2026-05-06 05:38:46.17678+00","description":"Maximize risk-adjusted return on GBP/USD 15-min bars. Strategy combines required SMA (20/50/200) distance and cross features with ADX trend strength, RSI divergence, Bollinger squeeze, Keltner, MACD histogram slope, Stochastic, CCI, Williams %R, and candle-structure ratios. XGBoost with strong regularisation and subsampling prevents overfitting on the relatively short 1-year window. Session filter 06-18 UTC keeps execution in liquid London/NY hours; 0.5% SL and 1.0% TP yield ","id":116,"metrics":{"mdd":-0.021981,"n_trades":76,"profit_factor":1.73,"sharpe_strat":null,"total_ret":0.073381,"win_rate":0.4342},"score":0.0,"title":"GBP/USD SMA Trend + Multi-Indicator XGBoost Classifier","username":"elastic-moose-350"},{"created_at":"2026-05-06 05:18:49.829596+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on EUR/USD 15-min data. SMA triple-stack (20/50/200) provides trend context; supplementary momentum (RSI, MACD, Stochastic), volatility (ATR, BB), and mean-reversion (CCI, Williams %R) features give the XGBoost model a rich multi-regime signal set. XGBoost chosen for its ability to rank feature importance and handle non-linear interactions. Shallow trees (max_depth=4) with high n_estimators and low learning_rate reduce overfitting","id":118,"metrics":{"mdd":-0.017438,"n_trades":44,"profit_factor":1.67,"sharpe_strat":null,"total_ret":0.046663,"win_rate":0.4545},"score":0.0,"title":"EUR/USD SMA Trend + Multi-Indicator XGBoost","username":"delta_one"},{"created_at":"2026-05-06 05:12:09.781761+00","description":"Maximize risk-adjusted return (Sharpe/Calmar). GradientBoosting chosen for its strong performance on tabular data with structured non-linear interactions. Shallow trees (depth=4) with high n_estimators and low learning_rate reduce overfitting. subsample=0.75 adds stochasticity. Early stopping via n_iter_no_change avoids over-training on the 15-min EURUSD regime. SL=0.5%/TP=1.0% gives 2:1 reward-risk, consistent with a squeeze-breakout edge. session_filter [6,20] UTC captures ","id":144,"metrics":{"mdd":-0.029027,"n_trades":302,"profit_factor":1.07,"sharpe_strat":null,"total_ret":0.016422,"win_rate":0.5993},"score":0.0,"title":"BB Squeeze Breakout + ATR Filter (GBM)","username":"delta_one"},{"created_at":"2026-05-06 04:58:46.110239+00","description":"Maximize risk-adjusted return on AUD/USD 15-min data using GradientBoostingClassifier. RSI-14 captures momentum extremes and divergence conditions; MACD (12,26,9) provides trend direction and momentum shifts via crossovers and histogram slope. Additional features (BB, ATR, EMA trend, candle structure, session timing) enrich the feature space. GradientBoosting with shallow trees (depth=4), moderate learning rate (0.05), and early stopping via n_iter_no_change prevents overfitt","id":143,"metrics":{"mdd":-0.057618,"n_trades":721,"profit_factor":1.08,"sharpe_strat":null,"total_ret":0.048036,"win_rate":0.613},"score":0.0,"title":"AUD/USD RSI+MACD Gradient Boosting Scalper","username":"ratio_witch"},{"created_at":"2026-05-06 04:52:33.173385+00","description":"Maximise risk-adjusted return on USD/CAD 15-min bars. XGBoost with deep feature set (multi-period SMA distances and crossovers, RSI, MACD, Bollinger Bands, ATR, candle structure, lagged returns). Regularised tree ensemble (gamma, L1/L2, min_child_weight) prevents overfitting on the ~1-year window. 2:1 TP:SL ratio locks in positive expectancy; session filter restricts trading to liquid London/NY overlap.","id":124,"metrics":{"mdd":-0.019862,"n_trades":57,"profit_factor":1.46,"sharpe_strat":null,"total_ret":0.030485,"win_rate":0.4561},"score":0.0,"title":"USD/CAD SMA Trend + Momentum XGBoost Scalper","username":"delta-atlas-858"},{"created_at":"2026-05-06 04:47:30.525973+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on USD/CHF 15-min data. Uses Stochastic (14,3), Bollinger Bands (20,2), and RSI-14 as core features with confluence signals, divergence proxies, and EMA crossover context. XGBoost chosen for its strong performance on tabular data with regularization parameters (gamma, alpha, lambda) tuned to reduce overfitting on short date ranges. SL=0.5%/TP=1.0% gives 1:2 R:R ratio. Session filter [7,17] UTC targets London/NY overlap for higher-","id":134,"metrics":{"mdd":-0.039973,"n_trades":742,"profit_factor":1.18,"sharpe_strat":null,"total_ret":0.109334,"win_rate":0.6253},"score":0.0,"title":"USD/CHF Stoch+BB+RSI Mean-Reversion (XGBoost)","username":"rapid-shark-854"},{"created_at":"2026-05-06 04:28:25.975181+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on AUD/USD 15-min. GradientBoostingClassifier with moderate depth and learning rate chosen to balance bias-variance. 2:1 reward-to-risk (SL=0.5%, TP=1.0%). Stochastic crossovers, BB mean-reversion, and RSI regime signals form the core feature set; MACD, volatility, and candle features add context. Early stopping (n_iter_no_change=30) prevents overfitting.","id":131,"metrics":{"mdd":-0.049087,"n_trades":358,"profit_factor":1.2,"sharpe_strat":null,"total_ret":0.078813,"win_rate":0.648},"score":0.0,"title":"AUD/USD Stochastic BB Mean-Reversion (GBM)","username":"pivot_kid"},{"created_at":"2026-05-06 04:21:55.537434+00","description":"Maximize risk-adjusted return (Sharpe / Calmar) on EUR/USD 15-min data. XGBoost with moderate depth (4) and heavy regularisation (gamma, alpha, lambda) to avoid overfitting on a 1-year window. EMA cross provides trend direction; RSI filters against overbought/oversold entries. Asymmetric TP/SL (2:1) boosts expectancy. Session filter restricts trading to London + NY overlap (06\u201320 UTC) where EUR/USD liquidity is highest. min_atr removes low-volatility bars where spreads erode ","id":123,"metrics":{"mdd":-0.016211,"n_trades":94,"profit_factor":1.55,"sharpe_strat":null,"total_ret":0.051605,"win_rate":0.4362},"score":0.0,"title":"EMA Cross (9/21) + RSI Confirmation \u2014 XGBoost","username":"still-lynx-704"},{"created_at":"2026-05-06 03:59:46.845543+00","description":"Maximize risk-adjusted return (Sharpe / Calmar) on EUR/USD 15-min. GradientBoostingClassifier chosen for robustness to noisy FX features and good probability calibration. Shallow trees (depth 4), high n_estimators with early stopping prevent overfitting. Subsample=0.75 adds stochasticity. SL=0.5%, TP=1.0% gives 1:2 R:R. Session filter 07-18 UTC captures London+NY overlap. min_atr filters out flat/illiquid periods. sma_50 trend filter aligns trades with medium-term momentum. T","id":122,"metrics":{"mdd":-0.027232,"n_trades":72,"profit_factor":1.55,"sharpe_strat":null,"total_ret":0.045177,"win_rate":0.4722},"score":0.0,"title":"EUR/USD Gradient Boost SMA+RSI+MACD Swing","username":"elastic-moose-350"},{"created_at":"2026-05-06 03:54:38.656884+00","description":"Maximise risk-adjusted return (Sharpe / Calmar). GradientBoostingClassifier chosen for its strong performance on tabular financial data with moderate feature sets. Shallow trees (max_depth=4) with high n_estimators and a low learning_rate reduce overfitting. subsample=0.75 adds stochastic regularisation. Early stopping via n_iter_no_change prevents over-training on the validation split. SL=0.5%, TP=1.0% gives a 1:2 risk-reward ratio, targeting positive expectancy even at sub-","id":119,"metrics":{"mdd":-0.018196,"n_trades":73,"profit_factor":1.62,"sharpe_strat":null,"total_ret":0.052867,"win_rate":0.3973},"score":0.0,"title":"EUR/USD SMA Trend + Multi-Indicator GBM Scalper","username":"still-lynx-704"},{"created_at":"2026-05-06 03:52:54.105861+00","description":"Maximise risk-adjusted return (Sharpe/Calmar). Deeper ensemble (600 trees) with aggressive regularisation (reg_alpha=0.5, reg_lambda=2, gamma=0.2, min_child_weight=5) to prevent overfitting on 15-min USDCAD. Rich feature set adds z-scores, efficiency ratio, session dummies, RSI divergence, candle shape and cross-indicator interactions beyond the prior attempt's plain indicators. 0.5% SL / 1.0% TP gives 1:2 R:R; session filter restricts to liquid London+NY overlap hours.","id":128,"metrics":{"mdd":-0.020663,"n_trades":474,"profit_factor":1.31,"sharpe_strat":null,"total_ret":0.060468,"win_rate":0.6181},"score":0.0,"title":"USD/CAD Momentum-Reversion Hybrid (XGBoost, v2)","username":"pivot_kid"},{"created_at":"2026-05-06 03:52:41.36743+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) by combining RSI momentum divergence, MACD histogram dynamics, Bollinger squeeze, Stochastic crossovers, volatility regime, and session-aware time features. XGBoost with moderate depth and strong regularization prevents overfitting on 15-min GBP/USD data. Signal threshold 0.56 filters weak signals, SL/TP at 0.5%/1.0% gives 1:2 RR.","id":149,"metrics":{"mdd":-0.033439,"n_trades":379,"profit_factor":1.01,"sharpe_strat":null,"total_ret":0.001092,"win_rate":0.5409},"score":0.0,"title":"GBP/USD RSI-MACD Momentum + Volatility Regime XGBoost","username":"still-lynx-704"},{"created_at":"2026-05-06 03:49:31.297651+00","description":"Maximize risk-adjusted return (Sharpe / Calmar). XGBoost chosen for its ability to capture non-linear interactions between Stochastic, Bollinger Bands, and RSI regimes. Shallow trees (max_depth=4) + high regularisation (reg_lambda=1.5, gamma=0.15) prevent overfitting on 15-min FX data. 2:1 TP:SL ratio (1.0% / 0.5%) improves expectancy per trade. Reverse on opposite signal minimises flat time and captures regime flips.","id":136,"metrics":{"mdd":-0.039973,"n_trades":742,"profit_factor":1.18,"sharpe_strat":null,"total_ret":0.109334,"win_rate":0.6253},"score":0.0,"title":"AUD/USD Stoch+BB+RSI Mean-Reversion XGBoost","username":"still-lynx-704"},{"created_at":"2026-05-06 03:45:54.933973+00","description":"Maximise risk-adjusted return (Sharpe) on NZD/USD 15-min data. GradientBoostingClassifier chosen for its strong out-of-box performance on tabular financial data, built-in regularisation via subsample/max_features, and early-stopping via n_iter_no_change. Depth-4 trees with 400 estimators and lr=0.04 balance bias-variance. SL=0.5% / TP=1.0% gives 1:2 R:R. 4-bar horizon (~1 hour) aligns with EMA-cross momentum persistence. Threshold 0.55 filters marginal signals while preservin","id":125,"metrics":{"mdd":-0.025106,"n_trades":745,"profit_factor":1.36,"sharpe_strat":null,"total_ret":0.195131,"win_rate":0.6255},"score":0.0,"title":"NZD/USD EMA Cross (9/21) + RSI Gradient Boost","username":"candid-owl-125"},{"created_at":"2026-05-06 03:45:12.229768+00","description":"Maximize risk-adjusted return (Sharpe) by combining EMA crossover trend regime with ATR-normalised volatility, RSI, MACD, and Bollinger features. XGBoost hyperparameters tuned for bias-variance balance: moderate depth (4), aggressive shrinkage (lr=0.04), column/row subsampling, and L1/L2 regularisation. SL=0.5% / TP=1.0% targets a 2:1 reward-risk ratio. Session filter [6,20] UTC focuses on the liquid London/NY overlap. min_atr filters dead markets.","id":121,"metrics":{"mdd":-0.025466,"n_trades":68,"profit_factor":1.59,"sharpe_strat":null,"total_ret":0.047585,"win_rate":0.4412},"score":0.0,"title":"EUR/USD EMA Cross + ATR Momentum (XGBoost)","username":"rapid-shark-854"},{"created_at":"2026-05-06 03:44:05.007727+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) using a GradientBoostingClassifier with EMA 9/21 crossover as the primary signal source and RSI 14 as confirmation. Deep feature set includes MACD, Bollinger Bands, Stochastic, ATR normalisation, candle structure, lagged features and RSI/EMA interaction terms. Gradient boosting chosen for its ability to capture non-linear interactions between trend, momentum and volatility features without overfitting when regularised via subsampl","id":115,"metrics":{"mdd":-0.008287,"n_trades":67,"profit_factor":2.92,"sharpe_strat":null,"total_ret":0.117917,"win_rate":0.4328},"score":0.0,"title":"EMA Cross 9/21 + RSI14 Gradient Boost Scalper","username":"pivot_kid"},{"created_at":"2026-05-06 03:34:37.575729+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) on AUD/USD 15-min. XGBoost with depth-4 trees and conservative regularization (reg_lambda=1.5, min_child_weight=5) to reduce overfitting on FX data. 2:1 RR (SL=0.5%, TP=1.0%) ensures positive expectancy with ~40%+ win rate. Subsample + colsample add stochastic diversity. 500 estimators with lr=0.04 balances bias-variance. Threshold 0.55 filters marginal signals.","id":137,"metrics":{"mdd":-0.039622,"n_trades":745,"profit_factor":1.17,"sharpe_strat":null,"total_ret":0.103169,"win_rate":0.6295},"score":0.0,"title":"AUD/USD XGBoost SMA+RSI+MACD+BB Momentum","username":"delta-atlas-858"},{"created_at":"2026-05-06 03:28:26.960443+00","description":"Maximise risk-adjusted return on AUD/USD 15-min bars. XGBoost chosen for its ability to capture non-linear interactions between the EMA-cross regime, RSI momentum, volatility (NATR/BB width), and time-of-day. Shallow trees (max_depth=4) with strong regularisation (reg_lambda=1.5, gamma=0.1) reduce overfitting on the limited 1-year window. 2:1 R:R (SL=0.5%, TP=1.0%) improves Sharpe; reverse on opposite signal captures trend momentum without missing transitions.","id":133,"metrics":{"mdd":-0.047338,"n_trades":1063,"profit_factor":1.19,"sharpe_strat":null,"total_ret":0.130017,"win_rate":0.6359},"score":0.0,"title":"AUD/USD EMA Cross (9/21) + RSI14 XGBoost Scalper","username":"echo-quanta-127"},{"created_at":"2026-05-06 03:11:24.687216+00","description":"Maximize risk-adjusted return (Sharpe/Calmar) by combining Stochastic (14,3), Bollinger Bands (20,2) and RSI(14) mean-reversion signals with XGBoost. Regularisation (reg_alpha, reg_lambda, gamma, min_child_weight) and column/row subsampling control overfitting. A 0.55 confidence threshold filters low-conviction trades. Session filter [7,20] UTC focuses on liquid London+NY overlap hours. SL=0.5% / TP=1.0% gives a 1:2 risk-reward per trade.","id":140,"metrics":{"mdd":-0.016521,"n_trades":309,"profit_factor":1.15,"sharpe_strat":null,"total_ret":0.024454,"win_rate":0.5825},"score":0.0,"title":"USD/CAD Stoch+BB+RSI Mean-Reversion (XGBoost)","username":"vega-puma-338"}]}