{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-Bar Classification\n", "\n", "Description:\n", "Here, we classify whether price will move up, down, or remain neutral over a fixed horizon (e.g., 5 bars). We label each bar with āˆ’1,0,+1 based on future returns exceeding positive/negative thresholds. A classifier (e.g., RandomForestClassifier) then learns to predict these discrete classes, which we translate into trading signals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### šŸ“Œ Important Note:\n", "This notebook contains *interactive charts generated using Vectorbt. \n", "GitHub does not display interactive Plotly charts, so the graphs will not be visible here. \n", "\n", "āœ… To view the charts, please download this notebook and run it on your local machine. \n", "Make sure you have Vectorbt and its dependencies installed to regenerate the visualizations.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Data & Feature Engineering\n", "\n", "**Objective:** \n", "Load raw price data (MetaTrader 5 or CSV) and transform it into a feature-rich dataset.\n", "\n", "**Tasks:**\n", "- Fetch historical bars \n", "- Apply `ta.add_all_ta_features` or custom features \n", "- (Optionally) create specific labels (multi-bar, double-barrier, regime, etc.) \n", "- Clean/prepare the final feature matrix **X** and target **y** " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "šŸ”µ Processing Symbol: US500\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011184 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 20939\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -2.577838\n", "[LightGBM] [Info] Start training from score -0.160961\n", "[LightGBM] [Info] Start training from score -2.620914\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " File \"c:\\Users\\moham\\miniconda3\\envs\\ml\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n", " cpu_info = subprocess.run(\n", " ^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\moham\\miniconda3\\envs\\ml\\Lib\\subprocess.py\", line 548, in run\n", " with Popen(*popenargs, **kwargs) as process:\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\moham\\miniconda3\\envs\\ml\\Lib\\subprocess.py\", line 1026, in __init__\n", " self._execute_child(args, executable, preexec_fn, close_fds,\n", " File \"c:\\Users\\moham\\miniconda3\\envs\\ml\\Lib\\subprocess.py\", line 1538, in _execute_child\n", " hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for US500: RandomForestClassifier with Sharpe 6.45\n", "\n", "\n", "šŸ”µ Processing Symbol: US2000\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.020595 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 21042\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -1.994600\n", "[LightGBM] [Info] Start training from score -0.322049\n", "[LightGBM] [Info] Start training from score -1.971343\n", "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for US2000: RandomForestClassifier with Sharpe 8.03\n", "\n", "\n", "šŸ”µ Processing Symbol: UK100\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019575 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 20900\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -2.910891\n", "[LightGBM] [Info] Start training from score -0.117833\n", "[LightGBM] [Info] Start training from score -2.869481\n", "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for UK100: RandomForestClassifier with Sharpe 5.93\n", "\n", "\n", "šŸ”µ Processing Symbol: JP225\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.017897 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 20960\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -1.884691\n", "[LightGBM] [Info] Start training from score -0.370998\n", "[LightGBM] [Info] Start training from score -1.844660\n", "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for JP225: RandomForestClassifier with Sharpe 8.83\n", "\n", "\n", "šŸ”µ Processing Symbol: XAUUSD\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004406 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 21068\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -2.641762\n", "[LightGBM] [Info] Start training from score -0.175164\n", "[LightGBM] [Info] Start training from score -2.414134\n", "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for XAUUSD: RandomForestClassifier with Sharpe 7.72\n", "\n", "\n", "šŸ”µ Processing Symbol: DE40\n", "āš” Training RandomForestClassifier...\n", "āš” Training GradientBoostingClassifier...\n", "āš” Training SVC...\n", "āš” Training XGBClassifier...\n", "āš” Training LGBMClassifier...\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.011010 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 20958\n", "[LightGBM] [Info] Number of data points in the train set: 9995, number of used features: 92\n", "[LightGBM] [Info] Start training from score -2.575207\n", "[LightGBM] [Info] Start training from score -0.171475\n", "[LightGBM] [Info] Start training from score -2.507880\n", "āš” Training GaussianNB...\n", "āš” Training BernoulliNB...\n", "šŸ† Best Model for DE40: RandomForestClassifier with Sharpe 7.01\n", "āœ… Saved best model for US500 (H1) to models/h1_models/US500_H1_best_model.pkl\n", "āœ… Saved best model for US2000 (H1) to models/h1_models/US2000_H1_best_model.pkl\n", "āœ… Saved best model for UK100 (H1) to models/h1_models/UK100_H1_best_model.pkl\n", "āœ… Saved best model for JP225 (H1) to models/h1_models/JP225_H1_best_model.pkl\n", "āœ… Saved best model for XAUUSD (H1) to models/h1_models/XAUUSD_H1_best_model.pkl\n", "āœ… Saved best model for DE40 (H1) to models/h1_models/DE40_H1_best_model.pkl\n", "\n", "\n", "āœ… Summary Results Across Symbols:\n" ] }, { "data": { "text/html": [ "
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SymbolModelAccuracyReturn(%)SharpeFittedModelScaler
35DE40RandomForestClassifier1.0008.3097.005(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
39DE40LGBMClassifier0.9887.4056.658LGBMClassifier(random_state=42)StandardScaler()
38DE40XGBClassifier0.9525.2745.681XGBClassifier(base_score=None, booster=None, c...StandardScaler()
36DE40GradientBoostingClassifier0.9243.9304.841([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
37DE40SVC0.8560.5771.752SVC(probability=True)StandardScaler()
40DE40GaussianNB0.7600.5281.257GaussianNB()StandardScaler()
41DE40BernoulliNB0.5730.0080.064BernoulliNB()StandardScaler()
21JP225RandomForestClassifier1.00059.5568.829(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
25JP225LGBMClassifier0.91225.4976.905LGBMClassifier(random_state=42)StandardScaler()
24JP225XGBClassifier0.85312.6215.383XGBClassifier(base_score=None, booster=None, c...StandardScaler()
22JP225GradientBoostingClassifier0.82810.2434.886([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
23JP225SVC0.7120.8601.147SVC(probability=True)StandardScaler()
26JP225GaussianNB0.6480.3240.614GaussianNB()StandardScaler()
27JP225BernoulliNB0.5320.2940.549BernoulliNB()StandardScaler()
14UK100RandomForestClassifier1.0003.6315.934(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
18UK100LGBMClassifier0.9963.4505.763LGBMClassifier(random_state=42)StandardScaler()
17UK100XGBClassifier0.9611.9413.950XGBClassifier(base_score=None, booster=None, c...StandardScaler()
15UK100GradientBoostingClassifier0.9501.9253.869([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
16UK100SVC0.8970.2431.386SVC(probability=True)StandardScaler()
19UK100GaussianNB0.8330.0950.357GaussianNB()StandardScaler()
20UK100BernoulliNB0.8080.0260.126BernoulliNB()StandardScaler()
7US2000RandomForestClassifier1.00046.2558.031(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
11US2000LGBMClassifier0.96129.8637.153LGBMClassifier(random_state=42)StandardScaler()
10US2000XGBClassifier0.91321.4326.570XGBClassifier(base_score=None, booster=None, c...StandardScaler()
8US2000GradientBoostingClassifier0.88316.3585.998([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
9US2000SVC0.7430.8301.237SVC(probability=True)StandardScaler()
12US2000GaussianNB0.6570.3040.532GaussianNB()StandardScaler()
13US2000BernoulliNB0.5740.2500.476BernoulliNB()StandardScaler()
0US500RandomForestClassifier1.0007.3416.453(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
4US500LGBMClassifier0.9946.6066.153LGBMClassifier(random_state=42)StandardScaler()
3US500XGBClassifier0.9594.3055.117XGBClassifier(base_score=None, booster=None, c...StandardScaler()
1US500GradientBoostingClassifier0.9403.7934.763([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
2US500SVC0.8650.6131.438SVC(probability=True)StandardScaler()
6US500BernoulliNB0.5770.3980.999BernoulliNB()StandardScaler()
5US500GaussianNB0.7720.3950.967GaussianNB()StandardScaler()
28XAUUSDRandomForestClassifier1.0007.9777.721(DecisionTreeClassifier(max_features='sqrt', r...StandardScaler()
32XAUUSDLGBMClassifier0.9856.6217.334LGBMClassifier(random_state=42)StandardScaler()
31XAUUSDXGBClassifier0.9404.4625.970XGBClassifier(base_score=None, booster=None, c...StandardScaler()
29XAUUSDGradientBoostingClassifier0.9183.3985.076([DecisionTreeRegressor(criterion='friedman_ms...StandardScaler()
30XAUUSDSVC0.8480.3861.846SVC(probability=True)StandardScaler()
33XAUUSDGaussianNB0.6040.3711.213GaussianNB()StandardScaler()
34XAUUSDBernoulliNB0.5120.1120.499BernoulliNB()StandardScaler()
\n", "
" ], "text/plain": [ " Symbol Model Accuracy Return(%) Sharpe \\\n", "35 DE40 RandomForestClassifier 1.000 8.309 7.005 \n", "39 DE40 LGBMClassifier 0.988 7.405 6.658 \n", "38 DE40 XGBClassifier 0.952 5.274 5.681 \n", "36 DE40 GradientBoostingClassifier 0.924 3.930 4.841 \n", "37 DE40 SVC 0.856 0.577 1.752 \n", "40 DE40 GaussianNB 0.760 0.528 1.257 \n", "41 DE40 BernoulliNB 0.573 0.008 0.064 \n", "21 JP225 RandomForestClassifier 1.000 59.556 8.829 \n", "25 JP225 LGBMClassifier 0.912 25.497 6.905 \n", "24 JP225 XGBClassifier 0.853 12.621 5.383 \n", "22 JP225 GradientBoostingClassifier 0.828 10.243 4.886 \n", "23 JP225 SVC 0.712 0.860 1.147 \n", "26 JP225 GaussianNB 0.648 0.324 0.614 \n", "27 JP225 BernoulliNB 0.532 0.294 0.549 \n", "14 UK100 RandomForestClassifier 1.000 3.631 5.934 \n", "18 UK100 LGBMClassifier 0.996 3.450 5.763 \n", "17 UK100 XGBClassifier 0.961 1.941 3.950 \n", "15 UK100 GradientBoostingClassifier 0.950 1.925 3.869 \n", "16 UK100 SVC 0.897 0.243 1.386 \n", "19 UK100 GaussianNB 0.833 0.095 0.357 \n", "20 UK100 BernoulliNB 0.808 0.026 0.126 \n", "7 US2000 RandomForestClassifier 1.000 46.255 8.031 \n", "11 US2000 LGBMClassifier 0.961 29.863 7.153 \n", "10 US2000 XGBClassifier 0.913 21.432 6.570 \n", "8 US2000 GradientBoostingClassifier 0.883 16.358 5.998 \n", "9 US2000 SVC 0.743 0.830 1.237 \n", "12 US2000 GaussianNB 0.657 0.304 0.532 \n", "13 US2000 BernoulliNB 0.574 0.250 0.476 \n", "0 US500 RandomForestClassifier 1.000 7.341 6.453 \n", "4 US500 LGBMClassifier 0.994 6.606 6.153 \n", "3 US500 XGBClassifier 0.959 4.305 5.117 \n", "1 US500 GradientBoostingClassifier 0.940 3.793 4.763 \n", "2 US500 SVC 0.865 0.613 1.438 \n", "6 US500 BernoulliNB 0.577 0.398 0.999 \n", "5 US500 GaussianNB 0.772 0.395 0.967 \n", "28 XAUUSD RandomForestClassifier 1.000 7.977 7.721 \n", "32 XAUUSD LGBMClassifier 0.985 6.621 7.334 \n", "31 XAUUSD XGBClassifier 0.940 4.462 5.970 \n", "29 XAUUSD GradientBoostingClassifier 0.918 3.398 5.076 \n", "30 XAUUSD SVC 0.848 0.386 1.846 \n", "33 XAUUSD GaussianNB 0.604 0.371 1.213 \n", "34 XAUUSD BernoulliNB 0.512 0.112 0.499 \n", "\n", " FittedModel Scaler \n", "35 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "39 LGBMClassifier(random_state=42) StandardScaler() \n", "38 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "36 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "37 SVC(probability=True) StandardScaler() \n", "40 GaussianNB() StandardScaler() \n", "41 BernoulliNB() StandardScaler() \n", "21 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "25 LGBMClassifier(random_state=42) StandardScaler() \n", "24 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "22 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "23 SVC(probability=True) StandardScaler() \n", "26 GaussianNB() StandardScaler() \n", "27 BernoulliNB() StandardScaler() \n", "14 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "18 LGBMClassifier(random_state=42) StandardScaler() \n", "17 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "15 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "16 SVC(probability=True) StandardScaler() \n", "19 GaussianNB() StandardScaler() \n", "20 BernoulliNB() StandardScaler() \n", "7 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "11 LGBMClassifier(random_state=42) StandardScaler() \n", "10 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "8 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "9 SVC(probability=True) StandardScaler() \n", "12 GaussianNB() StandardScaler() \n", "13 BernoulliNB() StandardScaler() \n", "0 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "4 LGBMClassifier(random_state=42) StandardScaler() \n", "3 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "1 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "2 SVC(probability=True) StandardScaler() \n", "6 BernoulliNB() StandardScaler() \n", "5 GaussianNB() StandardScaler() \n", "28 (DecisionTreeClassifier(max_features='sqrt', r... StandardScaler() \n", "32 LGBMClassifier(random_state=42) StandardScaler() \n", "31 XGBClassifier(base_score=None, booster=None, c... StandardScaler() \n", "29 ([DecisionTreeRegressor(criterion='friedman_ms... StandardScaler() \n", "30 SVC(probability=True) StandardScaler() \n", "33 GaussianNB() StandardScaler() \n", "34 BernoulliNB() StandardScaler() " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ----------------------------------------------------------------------------\n", "# 0) SETUP\n", "# ----------------------------------------------------------------------------\n", "import sys\n", "import os\n", "import warnings\n", "from pathlib import Path\n", "\n", "project_root = Path.cwd().parent.parent\n", "sys.path.append(str(project_root))\n", "os.chdir(str(project_root))\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import MetaTrader5 as mt5\n", "import vectorbt as vbt\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import joblib\n", "\n", "from data.data_loader_mt5 import get_data_mt5\n", "from features.feature_engineering import add_all_ta_features\n", "from features.labeling_schemes import create_labels_multi_bar\n", "from backtests.simple_backtest import simulate_trading, calculate_sharpe_ratio\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", "from sklearn.svm import SVC\n", "from xgboost import XGBClassifier\n", "from lightgbm import LGBMClassifier\n", "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n", "from sklearn.metrics import accuracy_score\n", "\n", "# ----------------------------------------------------------------------------\n", "# 1) LIST OF SYMBOLS\n", "# ----------------------------------------------------------------------------\n", "# symbols = [\"EURUSD\", \"USDJPY\", \"AUDUSD\", \"GBPUSD\", \"USDCHF\", \"EURGBP\", \"MSFT.NAS\", \"TSLA.NAS\", \"NVDA.NAS\", \"AMZN.NAS\", \"GOOG.NAS\", \"NFLX.NAS\",\"AAPL.NAS\", \n", "# \"BABA.NYSE\", \"JPM.NYSE\", \"XOM.NYSE\", \"BA.NYSE\", \"DIS.NYSE\", \"NKE.NYSE\"]\n", "\n", "\n", "symbols = [\"US500\", \"US2000\", \"UK100\", \"JP225\", \"XAUUSD\", \"DE40\"]\n", "timeframe = mt5.TIMEFRAME_H1\n", "n_bars = 10000\n", "start_pos = 0\n", "horizon = 5\n", "threshold = 0.005\n", "\n", "# ----------------------------------------------------------------------------\n", "# 2) BACKTESTING MULTIPLE SYMBOLS\n", "# ----------------------------------------------------------------------------\n", "if not mt5.initialize():\n", " raise RuntimeError(\"Failed to initialize MT5\")\n", "\n", "# Prepare folders\n", "model_folder = \"models/h1_models\"\n", "os.makedirs(model_folder, exist_ok=True)\n", "\n", "all_results = []\n", "best_models = {} # store best model per symbol\n", "\n", "for symbol in symbols:\n", " print(f\"\\n\\nšŸ”µ Processing Symbol: {symbol}\")\n", "\n", " data = get_data_mt5(symbol=symbol, timeframe=timeframe, n_bars=n_bars, start_pos=start_pos)\n", " if data is None or len(data) == 0:\n", " print(f\"āš ļø No data for {symbol}, skipping...\")\n", " continue\n", "\n", " df = add_all_ta_features(data)\n", " df_lbl = create_labels_multi_bar(df, horizon=horizon, threshold=threshold)\n", "\n", " X = df_lbl.drop(columns=[\"multi_bar_label\", \"future_return_h\"])\n", " y = df_lbl[\"multi_bar_label\"]\n", "\n", " scaler = StandardScaler()\n", " X_scaled = scaler.fit_transform(X)\n", "\n", " models = {\n", " \"RandomForestClassifier\": RandomForestClassifier(n_estimators=100, random_state=42),\n", " \"GradientBoostingClassifier\": GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=42),\n", " \"SVC\": SVC(C=1.0, kernel='rbf', probability=True),\n", " \"XGBClassifier\": XGBClassifier(n_estimators=100, learning_rate=0.1, random_state=42),\n", " \"LGBMClassifier\": LGBMClassifier(n_estimators=100, learning_rate=0.1, random_state=42),\n", " \"GaussianNB\": GaussianNB(),\n", " \"BernoulliNB\": BernoulliNB()\n", " }\n", "\n", " symbol_model_results = []\n", "\n", " for model_name, model in models.items():\n", " print(f\"āš” Training {model_name}...\")\n", "\n", " model.fit(X_scaled, y + 1)\n", "\n", " preds_shifted = model.predict(X_scaled)\n", " preds = preds_shifted - 1\n", "\n", " acc = accuracy_score(y, preds)\n", "\n", " signals = preds\n", " close_prices = df_lbl[\"close\"]\n", " signals_s = pd.Series(signals, index=close_prices.index)\n", "\n", " pf = vbt.Portfolio.from_signals(\n", " close_prices,\n", " entries=signals_s > 0,\n", " exits=signals_s < 0,\n", " init_cash=10000,\n", " freq='4H',\n", " fees=0.0002\n", " )\n", "\n", " total_return = pf.total_return()\n", " sharpe = pf.sharpe_ratio()\n", "\n", " symbol_model_results.append({\n", " \"Symbol\": symbol,\n", " \"Model\": model_name,\n", " \"Accuracy\": acc,\n", " \"Return(%)\": total_return,\n", " \"Sharpe\": sharpe,\n", " \"FittedModel\": model,\n", " \"Scaler\": scaler # keep scaler too\n", " })\n", "\n", " # Find best model by Sharpe for this symbol\n", " best_model_info = max(symbol_model_results, key=lambda x: x[\"Sharpe\"])\n", " best_models[symbol] = best_model_info\n", "\n", " print(f\"šŸ† Best Model for {symbol}: {best_model_info['Model']} with Sharpe {best_model_info['Sharpe']:.2f}\")\n", "\n", " all_results.extend(symbol_model_results)\n", "\n", "mt5.shutdown()\n", "\n", "# ----------------------------------------------------------------------------\n", "# 3) SAVE BEST MODELS\n", "# ----------------------------------------------------------------------------\n", "# Map timeframe integer to readable string\n", "timeframe_mapping = {\n", " mt5.TIMEFRAME_M1: \"M1\",\n", " mt5.TIMEFRAME_M5: \"M5\",\n", " mt5.TIMEFRAME_M15: \"M15\",\n", " mt5.TIMEFRAME_M30: \"M30\",\n", " mt5.TIMEFRAME_H1: \"H1\",\n", " mt5.TIMEFRAME_H4: \"H4\",\n", " mt5.TIMEFRAME_D1: \"D1\",\n", "}\n", "timeframe_str = timeframe_mapping.get(timeframe, str(timeframe)) # fallback if not mapped\n", "\n", "for symbol, info in best_models.items():\n", " save_path_model = f\"{model_folder}/{symbol}_{timeframe_str}_best_model.pkl\"\n", " joblib.dump({\n", " \"model\": info[\"FittedModel\"],\n", " \"scaler\": info[\"Scaler\"]\n", " }, save_path_model)\n", " print(f\"āœ… Saved best model for {symbol} ({timeframe_str}) to {save_path_model}\")\n", "\n", "\n", "# ----------------------------------------------------------------------------\n", "# 4) SUMMARY\n", "# ----------------------------------------------------------------------------\n", "summary_df = pd.DataFrame(all_results)\n", "summary_df = summary_df.sort_values(by=[\"Symbol\", \"Sharpe\"], ascending=[True, False])\n", "\n", "print(\"\\n\\nāœ… Summary Results Across Symbols:\")\n", "display(summary_df.round(3))\n", "\n", "# ----------------------------------------------------------------------------\n", "# 5) HEATMAP\n", "# ----------------------------------------------------------------------------\n", "pivot_df = summary_df.pivot(index=\"Model\", columns=\"Symbol\", values=\"Sharpe\")\n", "\n", "plt.figure(figsize=(12,6))\n", "sns.heatmap(pivot_df, annot=True, fmt=\".2f\", cmap=\"YlGnBu\")\n", "plt.title(\"Sharpe Ratio per Model per Symbol\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Backtesting & Performance Evaluation\n", "\n", "**Objective:** \n", "Evaluate how well the trained model performs on unseen data, simulating real trades.\n", "\n", "**Tasks:**\n", "- Use walk-forward or expanding splits to mimic ā€œliveā€ conditions \n", "- Convert model predictions to signals ([-1, 0, +1] or buy/sell/hold) \n", "- Run a simple backtest script or VectorBT for performance metrics \n", "- Calculate returns, Sharpe ratio, drawdowns, confusion matrix, etc. \n", "- Visualize results (equity curve, trades, etc.) to judge strategy viability " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "šŸ”µ Backtesting Symbol: US500\n", "\n", "āœ… Full Backtest Results:\n", "Accuracy=0.98, Return=2.71%, Sharpe=6.51\n", "Start 2024-06-25 23:00:00\n", "End 2025-05-01 02:00:00\n", "Period 832 days 12:00:00\n", "Start Value 10000.0\n", "End Value 37146.284683\n", "Total Return [%] 271.462847\n", "Benchmark Return [%] 2.520776\n", "Max Gross Exposure [%] 100.0\n", "Total Fees Paid 638.40476\n", "Max Drawdown [%] 2.021725\n", "Max Drawdown Duration 38 days 08:00:00\n", "Total Trades 82\n", "Total Closed Trades 81\n", "Total Open Trades 1\n", "Open Trade PnL 804.630346\n", "Win Rate [%] 97.530864\n", "Best Trade [%] 11.98042\n", "Worst Trade [%] -0.263292\n", "Avg Winning Trade [%] 1.663715\n", "Avg Losing Trade [%] -0.147076\n", "Avg Winning Trade Duration 6 days 01:46:19.746835443\n", "Avg Losing Trade Duration 0 days 12:00:00\n", "Profit Factor 359.6652\n", "Expectancy 325.205609\n", "Sharpe Ratio 6.513547\n", "Calmar Ratio 38.470046\n", "Omega Ratio 2.16382\n", "Sortino Ratio 15.540681\n", "dtype: object\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "0", "line": { "color": "#1f77b4" }, "name": "Close", "showlegend": true, "type": 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Position Id: %{customdata[1]}
Exit Timestamp: %{x}
PnL: %{customdata[2]:.6f}
Return: %{customdata[3]:.2%}", "legendgroup": "4", "marker": { "color": "#EA4335", "line": { "color": "rgb(181,31,18)", "width": 1 }, "opacity": [ 0.75, 0.7529176670720034 ], "size": [ 7, 7.136157796693488 ], "symbol": "circle" }, "mode": "markers", "name": "Closed - Loss", "type": "scatter", "uid": "4622c00b-d4be-4c49-ac2a-49e8bb5a0e45", "x": [ "2024-09-06T12:00:00", "2025-04-09T09:00:00" ], "xaxis": "x2", "y": [ -0.00030859723542758034, -0.002632919822142755 ], "yaxis": "y2" }, { "customdata": [ [ 81, 81, 804.6303462346841, 0.022145146856460976 ] ], "hovertemplate": "Exit Trade Id: %{customdata[0]}
Position Id: %{customdata[1]}
Exit Timestamp: %{x}
PnL: %{customdata[2]:.6f}
Return: %{customdata[3]:.2%}", "legendgroup": "5", "marker": { "color": "#FFAA00", "line": { "color": "rgb(178,118,0)", "width": 1 }, "opacity": [ 0.7774109033572211 ], "size": [ 8.27917549000365 ], "symbol": "circle" }, "mode": "markers", "name": "Open", "type": "scatter", "uid": "9cb8f6a5-82f9-4050-a81b-37af1b02c993", "x": [ "2025-05-01T02:00:00" ], "xaxis": "x2", "y": [ 0.022145146856460976 ], "yaxis": "y2" }, { "legendgroup": "6", "line": { "color": "#7f7f7f" }, "name": "Benchmark", "showlegend": true, "type": "scatter", "uid": "a647b768-3797-4474-bd3e-cc0f2b912bff", "x": [ "2024-06-25T23:00:00", "2024-06-26T01:00:00", "2024-06-26T02:00:00", "2024-06-26T03:00:00", "2024-06-26T04:00:00", "2024-06-26T05:00:00", "2024-06-26T06:00:00", "2024-06-26T07:00:00", "2024-06-26T08:00:00", "2024-06-26T09:00:00", "2024-06-26T10:00:00", "2024-06-26T11:00:00", "2024-06-26T12:00:00", "2024-06-26T13:00:00", "2024-06-26T14:00:00", "2024-06-26T15:00:00", "2024-06-26T16:00:00", "2024-06-26T17:00:00", 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Gross Exposure [%] 100.0\n", "Total Fees Paid 379.232223\n", "Max Drawdown [%] 1.056342\n", "Max Drawdown Duration 26 days 12:00:00\n", "Total Trades 64\n", "Total Closed Trades 64\n", "Total Open Trades 0\n", "Open Trade PnL 0.0\n", "Win Rate [%] 100.0\n", "Best Trade [%] 6.932756\n", "Worst Trade [%] 0.106444\n", "Avg Winning Trade [%] 1.361785\n", "Avg Losing Trade [%] NaN\n", "Avg Winning Trade Duration 6 days 17:11:15\n", "Avg Losing Trade Duration NaT\n", "Profit Factor inf\n", "Expectancy 213.400871\n", "Sharpe Ratio 5.701139\n", "Calmar Ratio 43.42266\n", "Omega Ratio 1.796142\n", "Sortino Ratio 12.243566\n", "dtype: object\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "0", "line": { "color": "#1f77b4" }, "name": "Close", "showlegend": true, "type": "scatter", "uid": "c89eb621-c6ab-44ca-9b61-3e580ad23ce8", "x": [ "2024-06-19T12:00:00", "2024-06-19T13:00:00", "2024-06-19T14:00:00", 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"Total Open Trades 1\n", "Open Trade PnL 2130.387181\n", "Win Rate [%] 97.315436\n", "Best Trade [%] 10.441019\n", "Worst Trade [%] -0.770533\n", "Avg Winning Trade [%] 1.667054\n", "Avg Losing Trade [%] -0.502653\n", "Avg Winning Trade Duration 2 days 21:35:59.999999999\n", "Avg Losing Trade Duration 0 days 09:00:00\n", "Profit Factor 98.33926\n", "Expectancy 643.562498\n", "Sharpe Ratio 8.148872\n", "Calmar Ratio 71.849301\n", "Omega Ratio 2.383043\n", "Sortino Ratio 19.578195\n", "dtype: object\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "legendgroup": "0", "line": { "color": "#1f77b4" }, "name": "Close", "showlegend": true, "type": "scatter", "uid": "a0ef3cac-a42e-4730-9cf0-a6b39f74cfba", "x": [ "2024-07-02T02:00:00", "2024-07-02T03:00:00", "2024-07-02T04:00:00", "2024-07-02T05:00:00", "2024-07-02T06:00:00", "2024-07-02T07:00:00", "2024-07-02T08:00:00", "2024-07-02T09:00:00", "2024-07-02T10:00:00", 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Position Id: %{customdata[1]}
Exit Timestamp: %{x}
PnL: %{customdata[2]:.6f}
Return: %{customdata[3]:.2%}", "legendgroup": "4", "marker": { "color": "#EA4335", "line": { "color": "rgb(181,31,18)", "width": 1 }, "opacity": [ 0.75, 0.7543587563654081 ], "size": [ 7, 7.203408630385709 ], "symbol": "circle" }, "mode": "markers", "name": "Closed - Loss", "type": "scatter", "uid": "bf1fa597-01c8-4006-a260-970e1c507188", "x": [ "2024-08-22T11:00:00", "2025-04-30T06:00:00" ], "xaxis": "x2", "y": [ -0.00012438703800635254, -0.00184409043385764 ], "yaxis": "y2" }, { "customdata": [ [ 70, 70, -131.14747172001807, -0.00381084471236524 ] ], "hovertemplate": "Exit Trade Id: %{customdata[0]}
Position Id: %{customdata[1]}
Exit Timestamp: %{x}
PnL: %{customdata[2]:.6f}
Return: %{customdata[3]:.2%}", "legendgroup": "5", "marker": { "color": "#FFAA00", "line": { "color": "rgb(178,118,0)", "width": 1 }, "opacity": [ 0.7593436873432264 ], "size": [ 7.436038742683901 ], "symbol": "circle" }, "mode": "markers", "name": "Open", "type": "scatter", "uid": "eb50bbe7-c269-4a39-87e9-1e7264de8d97", "x": [ "2025-05-01T02:00:00" ], "xaxis": "x2", "y": [ -0.00381084471236524 ], "yaxis": "y2" }, { "legendgroup": "6", "line": { "color": "#7f7f7f" }, "name": "Benchmark", "showlegend": true, "type": "scatter", "uid": "002b6007-6f96-4f97-9c26-5cd2bcb6ae95", "x": [ "2024-06-26T07:00:00", "2024-06-26T08:00:00", "2024-06-26T09:00:00", "2024-06-26T10:00:00", "2024-06-26T11:00:00", "2024-06-26T12:00:00", "2024-06-26T13:00:00", "2024-06-26T14:00:00", "2024-06-26T15:00:00", "2024-06-26T16:00:00", "2024-06-26T17:00:00", "2024-06-26T18:00:00", "2024-06-26T19:00:00", "2024-06-26T20:00:00", "2024-06-26T21:00:00", "2024-06-26T22:00:00", "2024-06-26T23:00:00", "2024-06-27T01:00:00", 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SymbolAccuracyReturn(%)Sharpe
4XAUUSD0.9912.4298.243
3JP2250.9669.8028.149
1US20000.9917.5257.794
5DE400.9682.8486.869
0US5000.9852.7156.514
2UK1000.9921.3665.701
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" ], "text/plain": [ " Symbol Accuracy Return(%) Sharpe\n", "4 XAUUSD 0.991 2.429 8.243\n", "3 JP225 0.966 9.802 8.149\n", "1 US2000 0.991 7.525 7.794\n", "5 DE40 0.968 2.848 6.869\n", "0 US500 0.985 2.715 6.514\n", "2 UK100 0.992 1.366 5.701" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# ----------------------------------------------------------------------------\n", "# 0) SETUP\n", "# ----------------------------------------------------------------------------\n", "import sys\n", "import os\n", "import warnings\n", "from pathlib import Path\n", "\n", "project_root = Path.cwd().parent.parent\n", "sys.path.append(str(project_root))\n", "os.chdir(str(project_root))\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import MetaTrader5 as mt5\n", "import vectorbt as vbt\n", "import joblib\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import plotly.graph_objects as go\n", "\n", "from data.data_loader_mt5 import get_data_mt5\n", "from features.feature_engineering import add_all_ta_features\n", "from features.labeling_schemes import create_labels_multi_bar\n", "\n", "# ----------------------------------------------------------------------------\n", "# 1) SYMBOLS TO BACKTEST\n", "# ----------------------------------------------------------------------------\n", "symbols = [\"US500\", \"US2000\", \"UK100\", \"JP225\", \"XAUUSD\", \"DE40\"]\n", "timeframe = mt5.TIMEFRAME_H1\n", "n_bars = 5000\n", "start_pos = 0\n", "horizon = 5\n", "threshold = 0.005\n", "\n", "model_folder = \"models/h1_models\"\n", "\n", "# ----------------------------------------------------------------------------\n", "# 2) BACKTESTING\n", "# ----------------------------------------------------------------------------\n", "if not mt5.initialize():\n", " raise RuntimeError(\"Failed to initialize MT5\")\n", "\n", "all_results = []\n", "\n", "for symbol in symbols:\n", " print(f\"\\n\\nšŸ”µ Backtesting Symbol: {symbol}\")\n", "\n", " model_path = f\"{model_folder}/{symbol}_H1_best_model.pkl\"\n", " if not os.path.exists(model_path):\n", " print(f\"āš ļø Model not found for {symbol}, skipping...\")\n", " continue\n", "\n", " # Load model + scaler\n", " loaded = joblib.load(model_path)\n", " model = loaded[\"model\"]\n", " scaler = loaded[\"scaler\"]\n", "\n", " # Fetch data\n", " data = get_data_mt5(symbol=symbol, timeframe=timeframe, n_bars=n_bars, start_pos=start_pos)\n", " if data is None or len(data) == 0:\n", " print(f\"āš ļø No data for {symbol}, skipping...\")\n", " continue\n", "\n", " df = add_all_ta_features(data)\n", " df_lbl = create_labels_multi_bar(df, horizon=horizon, threshold=threshold)\n", "\n", " X = df_lbl.drop(columns=[\"multi_bar_label\", \"future_return_h\"])\n", " y = df_lbl[\"multi_bar_label\"]\n", "\n", " X_scaled = scaler.transform(X)\n", "\n", " preds_shifted = model.predict(X_scaled)\n", " preds = preds_shifted - 1\n", "\n", " acc = (preds == y).mean()\n", "\n", " # Create signals\n", " close_prices = df_lbl[\"close\"]\n", " signals = preds\n", " signals_s = pd.Series(signals, index=close_prices.index)\n", "\n", " pf = vbt.Portfolio.from_signals(\n", " close=close_prices,\n", " entries=signals_s > 0,\n", " exits=signals_s < 0,\n", " init_cash=10000,\n", " freq=\"4H\",\n", " fees=0.0002\n", " )\n", "\n", " total_return = pf.total_return()\n", " sharpe = pf.sharpe_ratio()\n", "\n", " all_results.append({\n", " \"Symbol\": symbol,\n", " \"Accuracy\": acc,\n", " \"Return(%)\": total_return,\n", " \"Sharpe\": sharpe\n", " })\n", "\n", " # ----------------------------------------------------------------------------\n", " # 3) BACKTEST RESULTS\n", " # ----------------------------------------------------------------------------\n", " print(\"\\nāœ… Full Backtest Results:\")\n", " print(f\"Accuracy={acc:.2f}, Return={total_return:.2f}%, Sharpe={sharpe:.2f}\")\n", " print(pf.stats())\n", "\n", " # Plot Portfolio Equity Curve\n", " pf.plot(title=f\"Backtest: {symbol}\").show()\n", "\n", " # ----------------------------------------------------------------------------\n", " # 4) ACTUAL vs PREDICTED PRICE PLOT\n", " # ----------------------------------------------------------------------------\n", " print(\"\\nšŸ“ˆ Plotting Actual vs Predicted Close...\")\n", "\n", " actual_close = close_prices.copy()\n", "\n", " pred_movement = preds\n", " predicted_close = actual_close.copy()\n", "\n", " for idx in range(len(predicted_close) - horizon):\n", " window = pred_movement[idx: idx + horizon]\n", " predicted_close.iloc[idx + horizon] = predicted_close.iloc[idx] * (1 + 0.001 * window.sum())\n", "\n", " predicted_close = predicted_close.shift(-horizon)\n", "\n", " fig = go.Figure()\n", "\n", " fig.add_trace(go.Scatter(\n", " x=actual_close.index,\n", " y=actual_close,\n", " mode='lines',\n", " name='Actual Close',\n", " line=dict(color='black')\n", " ))\n", "\n", " fig.add_trace(go.Scatter(\n", " x=predicted_close.index,\n", " y=predicted_close,\n", " mode='lines',\n", " name='Predicted Close',\n", " line=dict(color='orange')\n", " ))\n", "\n", " fig.update_layout(\n", " title=f\"šŸ“ˆ Actual vs Predicted Close Price (horizon={horizon}) for {symbol}\",\n", " xaxis_title=\"Date\",\n", " yaxis_title=\"Price\",\n", " template=\"plotly_white\",\n", " legend=dict(x=0, y=1, bgcolor=\"rgba(255,255,255,0)\", bordercolor=\"Black\"),\n", " height=700\n", " )\n", "\n", " fig.show()\n", "\n", "mt5.shutdown()\n", "\n", "# ----------------------------------------------------------------------------\n", "# 5) FINAL SUMMARY\n", "# ----------------------------------------------------------------------------\n", "results_df = pd.DataFrame(all_results)\n", "results_df = results_df.sort_values(by=\"Sharpe\", ascending=False)\n", "\n", "print(\"\\n\\nāœ… Final Backtest Results Summary:\")\n", "display(results_df.round(3))\n", "\n", "# ----------------------------------------------------------------------------\n", "# 6) HEATMAP\n", "# ----------------------------------------------------------------------------\n", "\n", "pivot_df = results_df.set_index(\"Symbol\")[[\"Sharpe\"]] # <- fixed\n", "\n", "plt.figure(figsize=(8, 5))\n", "sns.heatmap(pivot_df, annot=True, fmt=\".2f\", cmap=\"YlGnBu\")\n", "plt.title(\"Sharpe Ratio per Symbol (Backtest)\")\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 3: Model Training & Hyperparameter Tuning\n", "\n", "**Objective:** \n", "Train an ML model (e.g., RandomForest, XGBoost) on the engineered features to predict the chosen labels.\n", "\n", "**Tasks:**\n", "- Perform time-based or walk-forward splits \n", "- Select top features if desired (e.g., using RandomForest feature importance) \n", "- Use `RandomizedSearchCV` or `GridSearchCV` to find optimal hyperparameters \n", "- Save the best model pipeline (e.g., `best_rf_pipeline.pkl`) " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "āš” Starting MT5 connection...\n", "\n", "\n", "šŸ”µ Optimizing Symbol: US30\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-20 20:03:10,194] A new study created in memory with name: no-name-725b04f3-aa18-4218-999d-7915391bbee9\n", "[I 2025-04-20 20:05:11,447] Trial 0 finished with value: 0.4966639978649587 and parameters: {'n_estimators': 700, 'max_depth': 11, 'min_samples_split': 11, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.4966639978649587.\n", "[I 2025-04-20 20:27:57,500] Trial 1 finished with value: 0.33626901521216973 and parameters: {'n_estimators': 500, 'max_depth': 22, 'min_samples_split': 12, 'max_features': None}. Best is trial 0 with value: 0.4966639978649587.\n", "[I 2025-04-20 20:30:29,470] Trial 2 finished with value: 0.4683746997598078 and parameters: {'n_estimators': 700, 'max_depth': 27, 'min_samples_split': 2, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.4966639978649587.\n", "[I 2025-04-20 20:32:43,239] Trial 3 finished with value: 0.4707766212970377 and parameters: {'n_estimators': 600, 'max_depth': 28, 'min_samples_split': 9, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.4966639978649587.\n", "[I 2025-04-20 20:36:35,073] Trial 4 finished with value: 0.46503869762476646 and parameters: {'n_estimators': 1000, 'max_depth': 18, 'min_samples_split': 13, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.4966639978649587.\n", "[I 2025-04-20 20:37:00,413] Trial 5 finished with value: 0.4981318388043768 and parameters: {'n_estimators': 100, 'max_depth': 11, 'min_samples_split': 13, 'max_features': 'sqrt'}. Best is trial 5 with value: 0.4981318388043768.\n", "[I 2025-04-20 20:52:06,463] Trial 6 finished with value: 0.47718174539631714 and parameters: {'n_estimators': 700, 'max_depth': 10, 'min_samples_split': 5, 'max_features': 0.5}. Best is trial 5 with value: 0.4981318388043768.\n", "[I 2025-04-20 21:31:52,840] Trial 7 finished with value: 0.3550840672538031 and parameters: {'n_estimators': 900, 'max_depth': 18, 'min_samples_split': 3, 'max_features': 0.8}. Best is trial 5 with value: 0.4981318388043768.\n", "[I 2025-04-20 21:33:31,075] Trial 8 finished with value: 0.4829196690685882 and parameters: {'n_estimators': 400, 'max_depth': 30, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 5 with value: 0.4981318388043768.\n", "[I 2025-04-20 22:26:27,220] Trial 9 finished with value: 0.3315986122231118 and parameters: {'n_estimators': 1000, 'max_depth': 26, 'min_samples_split': 6, 'max_features': None}. Best is trial 5 with value: 0.4981318388043768.\n", "[I 2025-04-20 22:26:37,533] Trial 10 finished with value: 0.5796637309847879 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5796637309847879.\n", "[I 2025-04-20 22:26:48,508] Trial 11 finished with value: 0.5796637309847879 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5796637309847879.\n", "[I 2025-04-20 22:27:02,577] Trial 12 finished with value: 0.5764611689351481 and parameters: {'n_estimators': 100, 'max_depth': 7, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5796637309847879.\n", "[I 2025-04-20 22:27:34,867] Trial 13 finished with value: 0.5801974913263944 and parameters: {'n_estimators': 300, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:28:40,336] Trial 14 finished with value: 0.48131838804376836 and parameters: {'n_estimators': 300, 'max_depth': 14, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:29:13,409] Trial 15 finished with value: 0.5797971710701895 and parameters: {'n_estimators': 300, 'max_depth': 5, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:36:50,588] Trial 16 finished with value: 0.5365625834000534 and parameters: {'n_estimators': 300, 'max_depth': 8, 'min_samples_split': 16, 'max_features': 0.8}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:42:46,433] Trial 17 finished with value: 0.36269015212169736 and parameters: {'n_estimators': 300, 'max_depth': 14, 'min_samples_split': 17, 'max_features': 0.5}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:43:48,960] Trial 18 finished with value: 0.4913263944488924 and parameters: {'n_estimators': 400, 'max_depth': 14, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:44:25,503] Trial 19 finished with value: 0.47504670402989063 and parameters: {'n_estimators': 200, 'max_depth': 22, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:45:22,066] Trial 20 finished with value: 0.563384040565786 and parameters: {'n_estimators': 500, 'max_depth': 8, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:45:36,950] Trial 21 finished with value: 0.5787296503869762 and parameters: {'n_estimators': 200, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 13 with value: 0.5801974913263944.\n", "[I 2025-04-20 22:45:53,110] Trial 22 finished with value: 0.5809981318388043 and parameters: {'n_estimators': 200, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 22:46:19,763] Trial 23 finished with value: 0.5519081932212436 and parameters: {'n_estimators': 200, 'max_depth': 9, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 22:46:59,484] Trial 24 finished with value: 0.5709901254336803 and parameters: {'n_estimators': 400, 'max_depth': 7, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 22:51:57,256] Trial 25 finished with value: 0.377902321857486 and parameters: {'n_estimators': 300, 'max_depth': 12, 'min_samples_split': 18, 'max_features': 0.5}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 22:55:13,065] Trial 26 finished with value: 0.5563117160394982 and parameters: {'n_estimators': 200, 'max_depth': 6, 'min_samples_split': 10, 'max_features': 0.8}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:06:02,161] Trial 27 finished with value: 0.46090205497731523 and parameters: {'n_estimators': 400, 'max_depth': 9, 'min_samples_split': 14, 'max_features': None}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:07:32,593] Trial 28 finished with value: 0.484921270349613 and parameters: {'n_estimators': 500, 'max_depth': 16, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:09:22,292] Trial 29 finished with value: 0.5006672004270082 and parameters: {'n_estimators': 600, 'max_depth': 12, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:09:47,311] Trial 30 finished with value: 0.5687216439818522 and parameters: {'n_estimators': 200, 'max_depth': 7, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:09:56,054] Trial 31 finished with value: 0.5793968508139845 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:10:12,579] Trial 32 finished with value: 0.5785962103015746 and parameters: {'n_estimators': 300, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:12:12,274] Trial 33 finished with value: 0.5528422738190552 and parameters: {'n_estimators': 100, 'max_depth': 7, 'min_samples_split': 17, 'max_features': None}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:12:46,250] Trial 34 finished with value: 0.5154790499065919 and parameters: {'n_estimators': 200, 'max_depth': 10, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:13:53,509] Trial 35 finished with value: 0.48078462770216174 and parameters: {'n_estimators': 300, 'max_depth': 20, 'min_samples_split': 8, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:15:32,068] Trial 36 finished with value: 0.5724579663730984 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 16, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:16:56,478] Trial 37 finished with value: 0.5310915398985855 and parameters: {'n_estimators': 100, 'max_depth': 9, 'min_samples_split': 13, 'max_features': 0.5}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:17:41,000] Trial 38 finished with value: 0.4854550306912196 and parameters: {'n_estimators': 200, 'max_depth': 11, 'min_samples_split': 19, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:38:44,468] Trial 39 finished with value: 0.33760341606618627 and parameters: {'n_estimators': 600, 'max_depth': 25, 'min_samples_split': 11, 'max_features': 0.8}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:46:38,261] Trial 40 finished with value: 0.5640512409927942 and parameters: {'n_estimators': 400, 'max_depth': 6, 'min_samples_split': 14, 'max_features': None}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:46:42,931] Trial 41 finished with value: 0.5796637309847879 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:46:49,580] Trial 42 finished with value: 0.5623165198825727 and parameters: {'n_estimators': 100, 'max_depth': 8, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:46:55,048] Trial 43 finished with value: 0.5788630904723779 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:47:05,902] Trial 44 finished with value: 0.573792367227115 and parameters: {'n_estimators': 200, 'max_depth': 6, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:47:26,572] Trial 45 finished with value: 0.5655190819322123 and parameters: {'n_estimators': 300, 'max_depth': 8, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:47:39,281] Trial 46 finished with value: 0.5264211369095276 and parameters: {'n_estimators': 100, 'max_depth': 10, 'min_samples_split': 20, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:47:51,957] Trial 47 finished with value: 0.5732586068855084 and parameters: {'n_estimators': 200, 'max_depth': 7, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-20 23:58:23,975] Trial 48 finished with value: 0.5644515612489992 and parameters: {'n_estimators': 300, 'max_depth': 6, 'min_samples_split': 12, 'max_features': 0.8}. Best is trial 22 with value: 0.5809981318388043.\n", "[I 2025-04-21 00:00:03,848] Trial 49 finished with value: 0.396850813984521 and parameters: {'n_estimators': 100, 'max_depth': 12, 'min_samples_split': 16, 'max_features': 0.5}. Best is trial 22 with value: 0.5809981318388043.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for US30: {'n_estimators': 200, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}\n", "šŸ’¾ Saved best model for US30 to models/rf_models\\US30_best_model.pkl\n", "\n", "\n", "šŸ”µ Optimizing Symbol: US500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-21 00:00:22,288] A new study created in memory with name: no-name-1e63308f-4a9a-46d6-8cd1-c64b6cde87a7\n", "[I 2025-04-21 00:01:37,208] Trial 0 finished with value: 0.4324793167867627 and parameters: {'n_estimators': 400, 'max_depth': 23, 'min_samples_split': 14, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.4324793167867627.\n", "[I 2025-04-21 00:30:08,876] Trial 1 finished with value: 0.3564184681078196 and parameters: {'n_estimators': 1000, 'max_depth': 14, 'min_samples_split': 10, 'max_features': None}. Best is trial 0 with value: 0.4324793167867627.\n", "[I 2025-04-21 00:33:53,872] Trial 2 finished with value: 0.3576194288764345 and parameters: {'n_estimators': 200, 'max_depth': 26, 'min_samples_split': 5, 'max_features': 0.5}. Best is trial 0 with value: 0.4324793167867627.\n", "[I 2025-04-21 00:44:00,634] Trial 3 finished with value: 0.47838270616493195 and parameters: {'n_estimators': 900, 'max_depth': 9, 'min_samples_split': 6, 'max_features': 0.5}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 00:45:37,557] Trial 4 finished with value: 0.355884707766213 and parameters: {'n_estimators': 100, 'max_depth': 23, 'min_samples_split': 6, 'max_features': 0.5}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 00:57:40,685] Trial 5 finished with value: 0.3692287163063785 and parameters: {'n_estimators': 900, 'max_depth': 25, 'min_samples_split': 3, 'max_features': 0.5}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 00:58:28,826] Trial 6 finished with value: 0.4360821990926074 and parameters: {'n_estimators': 500, 'max_depth': 19, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 01:02:20,834] Trial 7 finished with value: 0.3508139845209501 and parameters: {'n_estimators': 200, 'max_depth': 17, 'min_samples_split': 4, 'max_features': 0.8}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 01:19:28,264] Trial 8 finished with value: 0.33453429410194824 and parameters: {'n_estimators': 700, 'max_depth': 18, 'min_samples_split': 11, 'max_features': None}. Best is trial 3 with value: 0.47838270616493195.\n", "[I 2025-04-21 01:20:19,557] Trial 9 finished with value: 0.559914598345343 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 2, 'max_features': 'log2'}. Best is trial 9 with value: 0.559914598345343.\n", "[I 2025-04-21 01:20:52,614] Trial 10 finished with value: 0.5697891646650654 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5697891646650654.\n", "[I 2025-04-21 01:21:22,714] Trial 11 finished with value: 0.5697891646650654 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5697891646650654.\n", "[I 2025-04-21 01:21:57,769] Trial 12 finished with value: 0.5623165198825727 and parameters: {'n_estimators': 700, 'max_depth': 6, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5697891646650654.\n", "[I 2025-04-21 01:22:51,269] Trial 13 finished with value: 0.45329597010942085 and parameters: {'n_estimators': 700, 'max_depth': 11, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 10 with value: 0.5697891646650654.\n", "[I 2025-04-21 01:23:17,499] Trial 14 finished with value: 0.570322925006672 and parameters: {'n_estimators': 600, 'max_depth': 5, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:24:31,930] Trial 15 finished with value: 0.42153722978382707 and parameters: {'n_estimators': 500, 'max_depth': 30, 'min_samples_split': 15, 'max_features': 'sqrt'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:30:50,185] Trial 16 finished with value: 0.38911128903122494 and parameters: {'n_estimators': 400, 'max_depth': 12, 'min_samples_split': 17, 'max_features': 0.8}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:31:30,248] Trial 17 finished with value: 0.5024019215372298 and parameters: {'n_estimators': 600, 'max_depth': 9, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:32:42,695] Trial 18 finished with value: 0.43835068054443554 and parameters: {'n_estimators': 800, 'max_depth': 14, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:33:11,062] Trial 19 finished with value: 0.5361622631438484 and parameters: {'n_estimators': 400, 'max_depth': 8, 'min_samples_split': 8, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:33:46,693] Trial 20 finished with value: 0.5697891646650654 and parameters: {'n_estimators': 600, 'max_depth': 5, 'min_samples_split': 18, 'max_features': 'sqrt'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:34:20,714] Trial 21 finished with value: 0.569922604750467 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:35:21,995] Trial 22 finished with value: 0.4536962903656258 and parameters: {'n_estimators': 800, 'max_depth': 11, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 14 with value: 0.570322925006672.\n", "[I 2025-04-21 01:35:59,847] Trial 23 finished with value: 0.5708566853482786 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 01:37:02,244] Trial 24 finished with value: 0.5221510541766746 and parameters: {'n_estimators': 1000, 'max_depth': 8, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 01:53:37,923] Trial 25 finished with value: 0.36215639178009074 and parameters: {'n_estimators': 800, 'max_depth': 13, 'min_samples_split': 15, 'max_features': None}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:02:03,166] Trial 26 finished with value: 0.42780891379770486 and parameters: {'n_estimators': 600, 'max_depth': 10, 'min_samples_split': 13, 'max_features': 0.8}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:03:21,558] Trial 27 finished with value: 0.43848412062983727 and parameters: {'n_estimators': 800, 'max_depth': 16, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:04:12,522] Trial 28 finished with value: 0.5600480384307446 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:04:53,155] Trial 29 finished with value: 0.5461702695489725 and parameters: {'n_estimators': 500, 'max_depth': 7, 'min_samples_split': 14, 'max_features': 'sqrt'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:05:59,939] Trial 30 finished with value: 0.4382172404590339 and parameters: {'n_estimators': 600, 'max_depth': 21, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:06:33,035] Trial 31 finished with value: 0.5697891646650654 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:07:08,222] Trial 32 finished with value: 0.5697891646650654 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:19:57,370] Trial 33 finished with value: 0.49279423538831063 and parameters: {'n_estimators': 1000, 'max_depth': 7, 'min_samples_split': 16, 'max_features': None}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:20:45,370] Trial 34 finished with value: 0.49306111555911397 and parameters: {'n_estimators': 700, 'max_depth': 9, 'min_samples_split': 9, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:21:31,687] Trial 35 finished with value: 0.5632506004803842 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:24:02,304] Trial 36 finished with value: 0.44462236455831333 and parameters: {'n_estimators': 300, 'max_depth': 10, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:37:32,650] Trial 37 finished with value: 0.3652255137443288 and parameters: {'n_estimators': 800, 'max_depth': 14, 'min_samples_split': 18, 'max_features': 0.8}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:42:24,607] Trial 38 finished with value: 0.46090205497731523 and parameters: {'n_estimators': 600, 'max_depth': 9, 'min_samples_split': 13, 'max_features': 0.5}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:48:03,734] Trial 39 finished with value: 0.5378969842540698 and parameters: {'n_estimators': 500, 'max_depth': 6, 'min_samples_split': 19, 'max_features': None}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:49:19,324] Trial 40 finished with value: 0.43741659994662396 and parameters: {'n_estimators': 700, 'max_depth': 27, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:49:57,966] Trial 41 finished with value: 0.5704563650920736 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:50:41,618] Trial 42 finished with value: 0.5704563650920736 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:51:47,948] Trial 43 finished with value: 0.5340272217774219 and parameters: {'n_estimators': 1000, 'max_depth': 8, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:52:32,798] Trial 44 finished with value: 0.562583400053376 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:53:22,764] Trial 45 finished with value: 0.5551107552708833 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 20, 'max_features': 'log2'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:54:17,585] Trial 46 finished with value: 0.5700560448358687 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 18, 'max_features': 'sqrt'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:55:41,764] Trial 47 finished with value: 0.5210835334934614 and parameters: {'n_estimators': 900, 'max_depth': 8, 'min_samples_split': 18, 'max_features': 'sqrt'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:57:30,764] Trial 48 finished with value: 0.4725113424072591 and parameters: {'n_estimators': 1000, 'max_depth': 10, 'min_samples_split': 16, 'max_features': 'sqrt'}. Best is trial 23 with value: 0.5708566853482786.\n", "[I 2025-04-21 02:57:38,130] Trial 49 finished with value: 0.5671203629570323 and parameters: {'n_estimators': 100, 'max_depth': 6, 'min_samples_split': 11, 'max_features': 'sqrt'}. Best is trial 23 with value: 0.5708566853482786.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for US500: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}\n", "šŸ’¾ Saved best model for US500 to models/rf_models\\US500_best_model.pkl\n", "\n", "\n", "šŸ”µ Optimizing Symbol: DE40\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-21 02:58:08,014] A new study created in memory with name: no-name-44a35965-984f-4d7a-865d-60e3d04cb312\n", "[I 2025-04-21 02:58:21,529] Trial 0 finished with value: 0.3151344841429145 and parameters: {'n_estimators': 100, 'max_depth': 13, 'min_samples_split': 16, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.3151344841429145.\n", "[I 2025-04-21 02:59:14,799] Trial 1 finished with value: 0.32195905258932156 and parameters: {'n_estimators': 500, 'max_depth': 24, 'min_samples_split': 6, 'max_features': 'log2'}. Best is trial 1 with value: 0.32195905258932156.\n", "[I 2025-04-21 03:05:55,202] Trial 2 finished with value: 0.2965341897497658 and parameters: {'n_estimators': 300, 'max_depth': 15, 'min_samples_split': 11, 'max_features': None}. Best is trial 1 with value: 0.32195905258932156.\n", "[I 2025-04-21 03:09:32,661] Trial 3 finished with value: 0.37414692894419915 and parameters: {'n_estimators': 300, 'max_depth': 6, 'min_samples_split': 6, 'max_features': None}. Best is trial 3 with value: 0.37414692894419915.\n", "[I 2025-04-21 03:10:45,927] Trial 4 finished with value: 0.34885588117222 and parameters: {'n_estimators': 1000, 'max_depth': 9, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 3 with value: 0.37414692894419915.\n", "[I 2025-04-21 03:11:38,174] Trial 5 finished with value: 0.33681252509032517 and parameters: {'n_estimators': 600, 'max_depth': 12, 'min_samples_split': 2, 'max_features': 'log2'}. Best is trial 3 with value: 0.37414692894419915.\n", "[I 2025-04-21 03:21:27,950] Trial 6 finished with value: 0.2919844774521611 and parameters: {'n_estimators': 500, 'max_depth': 26, 'min_samples_split': 16, 'max_features': 0.8}. Best is trial 3 with value: 0.37414692894419915.\n", "[I 2025-04-21 03:27:31,511] Trial 7 finished with value: 0.4449350996922254 and parameters: {'n_estimators': 600, 'max_depth': 5, 'min_samples_split': 15, 'max_features': None}. Best is trial 7 with value: 0.4449350996922254.\n", "[I 2025-04-21 03:34:38,560] Trial 8 finished with value: 0.30228823765556 and parameters: {'n_estimators': 600, 'max_depth': 17, 'min_samples_split': 8, 'max_features': 0.5}. Best is trial 7 with value: 0.4449350996922254.\n", "[I 2025-04-21 03:35:32,260] Trial 9 finished with value: 0.35233507292921185 and parameters: {'n_estimators': 500, 'max_depth': 9, 'min_samples_split': 18, 'max_features': 'sqrt'}. Best is trial 7 with value: 0.4449350996922254.\n", "[I 2025-04-21 03:56:56,602] Trial 10 finished with value: 0.2934564431955038 and parameters: {'n_estimators': 900, 'max_depth': 22, 'min_samples_split': 20, 'max_features': None}. Best is trial 7 with value: 0.4449350996922254.\n", "[I 2025-04-21 03:58:56,169] Trial 11 finished with value: 0.4529640037468219 and parameters: {'n_estimators': 200, 'max_depth': 5, 'min_samples_split': 5, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:08:13,784] Trial 12 finished with value: 0.35621570988893353 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 14, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:10:51,689] Trial 13 finished with value: 0.3065703198180115 and parameters: {'n_estimators': 100, 'max_depth': 30, 'min_samples_split': 2, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:13:17,470] Trial 14 finished with value: 0.4472099558410277 and parameters: {'n_estimators': 300, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:17:20,641] Trial 15 finished with value: 0.3227619429947812 and parameters: {'n_estimators': 300, 'max_depth': 9, 'min_samples_split': 8, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:21:12,646] Trial 16 finished with value: 0.2959989294794594 and parameters: {'n_estimators': 200, 'max_depth': 19, 'min_samples_split': 13, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:25:12,584] Trial 17 finished with value: 0.31861367589990636 and parameters: {'n_estimators': 400, 'max_depth': 11, 'min_samples_split': 9, 'max_features': 0.5}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:27:21,355] Trial 18 finished with value: 0.3641107988759535 and parameters: {'n_estimators': 200, 'max_depth': 7, 'min_samples_split': 5, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:30:56,733] Trial 19 finished with value: 0.2978723404255319 and parameters: {'n_estimators': 200, 'max_depth': 15, 'min_samples_split': 13, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:35:47,288] Trial 20 finished with value: 0.30857754583166064 and parameters: {'n_estimators': 400, 'max_depth': 20, 'min_samples_split': 4, 'max_features': 0.5}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:42:54,728] Trial 21 finished with value: 0.4431955038137294 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 11, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:49:56,493] Trial 22 finished with value: 0.4405192024621973 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 15, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:55:46,609] Trial 23 finished with value: 0.3285159909005754 and parameters: {'n_estimators': 400, 'max_depth': 8, 'min_samples_split': 18, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:57:09,897] Trial 24 finished with value: 0.33212899772514387 and parameters: {'n_estimators': 700, 'max_depth': 11, 'min_samples_split': 9, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 04:57:56,583] Trial 25 finished with value: 0.4271376957045363 and parameters: {'n_estimators': 100, 'max_depth': 5, 'min_samples_split': 13, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:02:30,380] Trial 26 finished with value: 0.326241134751773 and parameters: {'n_estimators': 300, 'max_depth': 8, 'min_samples_split': 17, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:10:47,040] Trial 27 finished with value: 0.3012177171149471 and parameters: {'n_estimators': 400, 'max_depth': 13, 'min_samples_split': 12, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:13:38,990] Trial 28 finished with value: 0.322494312859628 and parameters: {'n_estimators': 200, 'max_depth': 10, 'min_samples_split': 10, 'max_features': 0.8}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:13:51,572] Trial 29 finished with value: 0.3095142513046969 and parameters: {'n_estimators': 100, 'max_depth': 14, 'min_samples_split': 15, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:17:48,522] Trial 30 finished with value: 0.34872206610464335 and parameters: {'n_estimators': 600, 'max_depth': 7, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:24:36,539] Trial 31 finished with value: 0.4431955038137294 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 11, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:35:13,468] Trial 32 finished with value: 0.35996253178107857 and parameters: {'n_estimators': 800, 'max_depth': 7, 'min_samples_split': 7, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:42:52,808] Trial 33 finished with value: 0.4469423257058745 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 4, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:43:47,650] Trial 34 finished with value: 0.3770908604308845 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 4, 'max_features': 'log2'}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 05:57:52,397] Trial 35 finished with value: 0.33012177171149476 and parameters: {'n_estimators': 800, 'max_depth': 10, 'min_samples_split': 3, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 06:09:15,615] Trial 36 finished with value: 0.3734778536063161 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 5, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 06:09:34,646] Trial 37 finished with value: 0.3687943262411348 and parameters: {'n_estimators': 300, 'max_depth': 8, 'min_samples_split': 6, 'max_features': 'log2'}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 06:21:28,967] Trial 38 finished with value: 0.3018867924528302 and parameters: {'n_estimators': 500, 'max_depth': 26, 'min_samples_split': 7, 'max_features': None}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 06:22:40,627] Trial 39 finished with value: 0.33815067576609126 and parameters: {'n_estimators': 600, 'max_depth': 12, 'min_samples_split': 3, 'max_features': 'sqrt'}. Best is trial 11 with value: 0.4529640037468219.\n", "[I 2025-04-21 06:23:23,867] Trial 40 finished with value: 0.5181319416566305 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 40 with value: 0.5181319416566305.\n", "[I 2025-04-21 06:24:02,364] Trial 41 finished with value: 0.5198715375351265 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 41 with value: 0.5198715375351265.\n", "[I 2025-04-21 06:24:53,101] Trial 42 finished with value: 0.36718854543021545 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 41 with value: 0.5198715375351265.\n", "[I 2025-04-21 06:25:43,376] Trial 43 finished with value: 0.3895356617155092 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 41 with value: 0.5198715375351265.\n", "[I 2025-04-21 06:26:48,092] Trial 44 finished with value: 0.3476515455640305 and parameters: {'n_estimators': 900, 'max_depth': 9, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 41 with value: 0.5198715375351265.\n", "[I 2025-04-21 06:27:22,230] Trial 45 finished with value: 0.5205406128730096 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 45 with value: 0.5205406128730096.\n", "[I 2025-04-21 06:28:06,835] Trial 46 finished with value: 0.3849859494179044 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 18, 'max_features': 'log2'}. Best is trial 45 with value: 0.5205406128730096.\n", "[I 2025-04-21 06:29:12,809] Trial 47 finished with value: 0.3586243811053125 and parameters: {'n_estimators': 1000, 'max_depth': 8, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 45 with value: 0.5205406128730096.\n", "[I 2025-04-21 06:30:11,476] Trial 48 finished with value: 0.3377492305633614 and parameters: {'n_estimators': 800, 'max_depth': 10, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 45 with value: 0.5205406128730096.\n", "[I 2025-04-21 06:30:55,341] Trial 49 finished with value: 0.38726080556670683 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 45 with value: 0.5205406128730096.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for DE40: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 18, 'max_features': 'log2'}\n", "šŸ’¾ Saved best model for DE40 to models/rf_models\\DE40_best_model.pkl\n", "\n", "\n", "šŸ”µ Optimizing Symbol: JP225\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-21 06:31:25,383] A new study created in memory with name: no-name-163d554e-5fad-4959-9afd-fd0dae7ec105\n", "[I 2025-04-21 06:34:32,354] Trial 0 finished with value: 0.34414198025086734 and parameters: {'n_estimators': 300, 'max_depth': 7, 'min_samples_split': 20, 'max_features': 0.8}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 06:35:16,824] Trial 1 finished with value: 0.32412596744061917 and parameters: {'n_estimators': 300, 'max_depth': 16, 'min_samples_split': 19, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 06:39:15,076] Trial 2 finished with value: 0.3142514011208967 and parameters: {'n_estimators': 200, 'max_depth': 26, 'min_samples_split': 18, 'max_features': 0.8}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 06:44:06,906] Trial 3 finished with value: 0.3121163597544703 and parameters: {'n_estimators': 400, 'max_depth': 23, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 06:56:11,054] Trial 4 finished with value: 0.3147851614625033 and parameters: {'n_estimators': 1000, 'max_depth': 21, 'min_samples_split': 9, 'max_features': 0.5}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 06:57:25,837] Trial 5 finished with value: 0.3286629303442754 and parameters: {'n_estimators': 600, 'max_depth': 13, 'min_samples_split': 11, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 07:15:19,934] Trial 6 finished with value: 0.31772084334133976 and parameters: {'n_estimators': 800, 'max_depth': 24, 'min_samples_split': 3, 'max_features': 0.8}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 07:15:31,194] Trial 7 finished with value: 0.31091539898585535 and parameters: {'n_estimators': 100, 'max_depth': 24, 'min_samples_split': 8, 'max_features': 'log2'}. Best is trial 0 with value: 0.34414198025086734.\n", "[I 2025-04-21 07:16:06,286] Trial 8 finished with value: 0.3550840672538031 and parameters: {'n_estimators': 500, 'max_depth': 8, 'min_samples_split': 4, 'max_features': 'log2'}. Best is trial 8 with value: 0.3550840672538031.\n", "[I 2025-04-21 07:27:06,227] Trial 9 finished with value: 0.31785428342674144 and parameters: {'n_estimators': 400, 'max_depth': 21, 'min_samples_split': 3, 'max_features': None}. Best is trial 8 with value: 0.3550840672538031.\n", "[I 2025-04-21 07:27:48,803] Trial 10 finished with value: 0.3482786228983186 and parameters: {'n_estimators': 600, 'max_depth': 9, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 8 with value: 0.3550840672538031.\n", "[I 2025-04-21 07:28:20,134] Trial 11 finished with value: 0.3713637576728049 and parameters: {'n_estimators': 600, 'max_depth': 6, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 11 with value: 0.3713637576728049.\n", "[I 2025-04-21 07:28:56,132] Trial 12 finished with value: 0.4067253803042434 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 12 with value: 0.4067253803042434.\n", "[I 2025-04-21 07:29:31,780] Trial 13 finished with value: 0.40218841740058714 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 12 with value: 0.4067253803042434.\n", "[I 2025-04-21 07:30:51,949] Trial 14 finished with value: 0.3167867627435281 and parameters: {'n_estimators': 800, 'max_depth': 30, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 12 with value: 0.4067253803042434.\n", "[I 2025-04-21 07:53:42,877] Trial 15 finished with value: 0.3294635708566854 and parameters: {'n_estimators': 800, 'max_depth': 12, 'min_samples_split': 16, 'max_features': None}. Best is trial 12 with value: 0.4067253803042434.\n", "[I 2025-04-21 07:55:02,540] Trial 16 finished with value: 0.40872698158526816 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 07:57:41,081] Trial 17 finished with value: 0.3436082199092607 and parameters: {'n_estimators': 1000, 'max_depth': 12, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:00:36,939] Trial 18 finished with value: 0.32519348812383236 and parameters: {'n_estimators': 900, 'max_depth': 16, 'min_samples_split': 7, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:14:13,259] Trial 19 finished with value: 0.348145182812917 and parameters: {'n_estimators': 900, 'max_depth': 9, 'min_samples_split': 6, 'max_features': 0.5}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:27:43,505] Trial 20 finished with value: 0.36415799306111557 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 12, 'max_features': None}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:30:04,861] Trial 21 finished with value: 0.34801174272751534 and parameters: {'n_estimators': 900, 'max_depth': 10, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:31:12,107] Trial 22 finished with value: 0.40659194021884176 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:32:18,053] Trial 23 finished with value: 0.40592473979183347 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:36:24,377] Trial 24 finished with value: 0.3453429410194822 and parameters: {'n_estimators': 1000, 'max_depth': 11, 'min_samples_split': 13, 'max_features': 'sqrt'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:39:18,161] Trial 25 finished with value: 0.3315986122231118 and parameters: {'n_estimators': 700, 'max_depth': 14, 'min_samples_split': 10, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:42:00,688] Trial 26 finished with value: 0.3576194288764345 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 17, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:44:18,338] Trial 27 finished with value: 0.35308246597277826 and parameters: {'n_estimators': 700, 'max_depth': 8, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 08:48:13,513] Trial 28 finished with value: 0.35068054443554847 and parameters: {'n_estimators': 1000, 'max_depth': 10, 'min_samples_split': 10, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:04:50,518] Trial 29 finished with value: 0.34921270349613026 and parameters: {'n_estimators': 500, 'max_depth': 7, 'min_samples_split': 13, 'max_features': 0.8}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:18:57,343] Trial 30 finished with value: 0.34734454230050704 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 17, 'max_features': 0.5}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:19:54,805] Trial 31 finished with value: 0.40592473979183347 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:21:20,984] Trial 32 finished with value: 0.3755004003202562 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:22:52,843] Trial 33 finished with value: 0.4056578596210301 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 10, 'max_features': 'sqrt'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:38:02,600] Trial 34 finished with value: 0.3478783026421137 and parameters: {'n_estimators': 600, 'max_depth': 8, 'min_samples_split': 14, 'max_features': 0.8}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:39:38,971] Trial 35 finished with value: 0.32359220709901254 and parameters: {'n_estimators': 500, 'max_depth': 18, 'min_samples_split': 19, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:41:02,961] Trial 36 finished with value: 0.3772351214304777 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 9, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:43:03,099] Trial 37 finished with value: 0.3282626100880704 and parameters: {'n_estimators': 400, 'max_depth': 15, 'min_samples_split': 11, 'max_features': 'sqrt'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 09:56:00,583] Trial 38 finished with value: 0.3424072591406459 and parameters: {'n_estimators': 700, 'max_depth': 9, 'min_samples_split': 15, 'max_features': 0.5}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 10:08:51,169] Trial 39 finished with value: 0.3154523618895116 and parameters: {'n_estimators': 300, 'max_depth': 18, 'min_samples_split': 12, 'max_features': 0.8}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:08:27,787] Trial 40 finished with value: 0.3113157192420603 and parameters: {'n_estimators': 1000, 'max_depth': 28, 'min_samples_split': 13, 'max_features': None}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:09:36,498] Trial 41 finished with value: 0.40592473979183347 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:10:46,649] Trial 42 finished with value: 0.3713637576728049 and parameters: {'n_estimators': 600, 'max_depth': 6, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:12:38,759] Trial 43 finished with value: 0.36776087536696017 and parameters: {'n_estimators': 800, 'max_depth': 7, 'min_samples_split': 9, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:13:46,105] Trial 44 finished with value: 0.40805978115825997 and parameters: {'n_estimators': 600, 'max_depth': 5, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:15:24,668] Trial 45 finished with value: 0.3480117427275154 and parameters: {'n_estimators': 600, 'max_depth': 8, 'min_samples_split': 8, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:16:59,861] Trial 46 finished with value: 0.34801174272751534 and parameters: {'n_estimators': 500, 'max_depth': 10, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:19:31,091] Trial 47 finished with value: 0.3191886842807579 and parameters: {'n_estimators': 600, 'max_depth': 20, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:21:09,077] Trial 48 finished with value: 0.37683480117427276 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.40872698158526816.\n", "[I 2025-04-21 11:37:59,968] Trial 49 finished with value: 0.3474779823859087 and parameters: {'n_estimators': 500, 'max_depth': 8, 'min_samples_split': 15, 'max_features': None}. Best is trial 16 with value: 0.40872698158526816.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for JP225: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 12, 'max_features': 'log2'}\n", "šŸ’¾ Saved best model for JP225 to models/rf_models\\JP225_best_model.pkl\n", "\n", "\n", "šŸ”µ Optimizing Symbol: UK100\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-21 11:39:21,953] A new study created in memory with name: no-name-f6502bb0-70e9-41e8-85cf-08ecf4401174\n", "[I 2025-04-21 11:41:47,988] Trial 0 finished with value: 0.4531502423263327 and parameters: {'n_estimators': 600, 'max_depth': 25, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 0 with value: 0.4531502423263327.\n", "[I 2025-04-21 11:44:23,487] Trial 1 finished with value: 0.46607431340872374 and parameters: {'n_estimators': 800, 'max_depth': 7, 'min_samples_split': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 11:51:44,936] Trial 2 finished with value: 0.4254173397953689 and parameters: {'n_estimators': 200, 'max_depth': 12, 'min_samples_split': 4, 'max_features': 0.8}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 11:55:55,942] Trial 3 finished with value: 0.44803446418955306 and parameters: {'n_estimators': 700, 'max_depth': 23, 'min_samples_split': 10, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 12:37:59,558] Trial 4 finished with value: 0.41168551427032846 and parameters: {'n_estimators': 700, 'max_depth': 20, 'min_samples_split': 7, 'max_features': None}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 12:42:51,355] Trial 5 finished with value: 0.44938072159396875 and parameters: {'n_estimators': 800, 'max_depth': 28, 'min_samples_split': 8, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 12:53:00,607] Trial 6 finished with value: 0.420032310177706 and parameters: {'n_estimators': 200, 'max_depth': 13, 'min_samples_split': 13, 'max_features': None}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 13:39:28,907] Trial 7 finished with value: 0.4106085083467959 and parameters: {'n_estimators': 800, 'max_depth': 20, 'min_samples_split': 4, 'max_features': None}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 13:40:00,443] Trial 8 finished with value: 0.4659396876682822 and parameters: {'n_estimators': 300, 'max_depth': 6, 'min_samples_split': 14, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 13:40:27,301] Trial 9 finished with value: 0.44507269789983844 and parameters: {'n_estimators': 200, 'max_depth': 22, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 1 with value: 0.46607431340872374.\n", "[I 2025-04-21 13:48:57,573] Trial 10 finished with value: 0.5317716747442112 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 14:00:59,352] Trial 11 finished with value: 0.5317716747442112 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 14:23:49,410] Trial 12 finished with value: 0.43847603661820145 and parameters: {'n_estimators': 1000, 'max_depth': 11, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 14:34:33,656] Trial 13 finished with value: 0.5317716747442112 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 14:59:23,415] Trial 14 finished with value: 0.42932148626817446 and parameters: {'n_estimators': 1000, 'max_depth': 15, 'min_samples_split': 17, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 15:06:24,632] Trial 15 finished with value: 0.4430533117932149 and parameters: {'n_estimators': 400, 'max_depth': 9, 'min_samples_split': 17, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 15:29:56,190] Trial 16 finished with value: 0.4250134625740441 and parameters: {'n_estimators': 900, 'max_depth': 16, 'min_samples_split': 17, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 15:44:26,869] Trial 17 finished with value: 0.44022617124394187 and parameters: {'n_estimators': 500, 'max_depth': 8, 'min_samples_split': 18, 'max_features': 0.8}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 16:00:14,763] Trial 18 finished with value: 0.4481690899299946 and parameters: {'n_estimators': 900, 'max_depth': 9, 'min_samples_split': 15, 'max_features': 0.5}. Best is trial 10 with value: 0.5317716747442112.\n", "[I 2025-04-21 16:11:00,614] Trial 19 finished with value: 0.5319063004846526 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 19 with value: 0.5319063004846526.\n", "[I 2025-04-21 16:23:34,539] Trial 20 finished with value: 0.43941841680129246 and parameters: {'n_estimators': 600, 'max_depth': 10, 'min_samples_split': 18, 'max_features': 0.5}. Best is trial 19 with value: 0.5319063004846526.\n", "[I 2025-04-21 16:34:59,729] Trial 21 finished with value: 0.5319063004846526 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 19 with value: 0.5319063004846526.\n", "[I 2025-04-21 16:46:45,700] Trial 22 finished with value: 0.5327140549273021 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 17:03:54,676] Trial 23 finished with value: 0.460016155088853 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 16, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 17:22:33,028] Trial 24 finished with value: 0.4291868605277329 and parameters: {'n_estimators': 700, 'max_depth': 14, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 17:48:33,198] Trial 25 finished with value: 0.4437264404954227 and parameters: {'n_estimators': 900, 'max_depth': 8, 'min_samples_split': 18, 'max_features': 0.8}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 17:51:18,009] Trial 26 finished with value: 0.459073774905762 and parameters: {'n_estimators': 800, 'max_depth': 11, 'min_samples_split': 16, 'max_features': 'log2'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 18:16:57,437] Trial 27 finished with value: 0.41801292407108237 and parameters: {'n_estimators': 700, 'max_depth': 30, 'min_samples_split': 2, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 18:25:48,303] Trial 28 finished with value: 0.46028540656973616 and parameters: {'n_estimators': 500, 'max_depth': 7, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 18:28:15,717] Trial 29 finished with value: 0.4550350026925148 and parameters: {'n_estimators': 600, 'max_depth': 17, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 18:39:16,858] Trial 30 finished with value: 0.5249057619816909 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 15, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 18:53:22,834] Trial 31 finished with value: 0.4726709746903608 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 19:02:41,638] Trial 32 finished with value: 0.5320409262250942 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 19:19:26,800] Trial 33 finished with value: 0.44964997307485194 and parameters: {'n_estimators': 800, 'max_depth': 9, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 19:42:19,806] Trial 34 finished with value: 0.44938072159396875 and parameters: {'n_estimators': 800, 'max_depth': 7, 'min_samples_split': 18, 'max_features': 0.8}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 20:24:02,015] Trial 35 finished with value: 0.4205708131394723 and parameters: {'n_estimators': 900, 'max_depth': 11, 'min_samples_split': 16, 'max_features': None}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 20:26:21,777] Trial 36 finished with value: 0.4614970382337103 and parameters: {'n_estimators': 700, 'max_depth': 8, 'min_samples_split': 12, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 20:44:01,184] Trial 37 finished with value: 0.4326871297792138 and parameters: {'n_estimators': 800, 'max_depth': 13, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 20:46:36,912] Trial 38 finished with value: 0.4520732364028002 and parameters: {'n_estimators': 700, 'max_depth': 25, 'min_samples_split': 7, 'max_features': 'log2'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:09:28,261] Trial 39 finished with value: 0.4701130856219709 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 9, 'max_features': None}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:09:51,818] Trial 40 finished with value: 0.4641895530425417 and parameters: {'n_estimators': 100, 'max_depth': 10, 'min_samples_split': 17, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:19:11,159] Trial 41 finished with value: 0.5317716747442112 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:28:34,676] Trial 42 finished with value: 0.4768443726440495 and parameters: {'n_estimators': 800, 'max_depth': 6, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:38:53,926] Trial 43 finished with value: 0.5317716747442112 and parameters: {'n_estimators': 1000, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 21:52:16,586] Trial 44 finished with value: 0.45893914916532036 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 18, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 22:16:24,093] Trial 45 finished with value: 0.41841680129240705 and parameters: {'n_estimators': 1000, 'max_depth': 19, 'min_samples_split': 19, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 22:23:17,442] Trial 46 finished with value: 0.5312331717824449 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 20, 'max_features': 0.5}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 22:46:58,072] Trial 47 finished with value: 0.43874528809908453 and parameters: {'n_estimators': 900, 'max_depth': 8, 'min_samples_split': 17, 'max_features': None}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 22:49:44,761] Trial 48 finished with value: 0.46620893914916534 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 19, 'max_features': 'sqrt'}. Best is trial 22 with value: 0.5327140549273021.\n", "[I 2025-04-21 23:09:49,162] Trial 49 finished with value: 0.4378029079159935 and parameters: {'n_estimators': 800, 'max_depth': 10, 'min_samples_split': 5, 'max_features': 0.8}. Best is trial 22 with value: 0.5327140549273021.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for UK100: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 19, 'max_features': 0.5}\n", "šŸ’¾ Saved best model for UK100 to models/rf_models\\UK100_best_model.pkl\n", "\n", "\n", "šŸ”µ Optimizing Symbol: XAUUSD\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[I 2025-04-21 23:16:53,783] A new study created in memory with name: no-name-ff9e7233-2643-4a14-8e74-706391003f43\n", "[I 2025-04-21 23:17:23,187] Trial 0 finished with value: 0.37950360288230583 and parameters: {'n_estimators': 100, 'max_depth': 24, 'min_samples_split': 17, 'max_features': 'sqrt'}. Best is trial 0 with value: 0.37950360288230583.\n", "[I 2025-04-21 23:52:04,209] Trial 1 finished with value: 0.4551641313050441 and parameters: {'n_estimators': 1000, 'max_depth': 19, 'min_samples_split': 20, 'max_features': 0.8}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-21 23:53:05,934] Trial 2 finished with value: 0.39738457432612756 and parameters: {'n_estimators': 400, 'max_depth': 17, 'min_samples_split': 5, 'max_features': 'log2'}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-21 23:53:51,510] Trial 3 finished with value: 0.40872698158526816 and parameters: {'n_estimators': 200, 'max_depth': 19, 'min_samples_split': 3, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-21 23:57:17,556] Trial 4 finished with value: 0.39178009073925807 and parameters: {'n_estimators': 900, 'max_depth': 20, 'min_samples_split': 19, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-22 00:04:54,694] Trial 5 finished with value: 0.4314117961035495 and parameters: {'n_estimators': 500, 'max_depth': 13, 'min_samples_split': 18, 'max_features': 0.5}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-22 00:35:42,417] Trial 6 finished with value: 0.45102748865759273 and parameters: {'n_estimators': 800, 'max_depth': 30, 'min_samples_split': 18, 'max_features': None}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-22 00:44:15,158] Trial 7 finished with value: 0.43888444088604217 and parameters: {'n_estimators': 300, 'max_depth': 18, 'min_samples_split': 9, 'max_features': 0.8}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-22 00:44:34,520] Trial 8 finished with value: 0.429276754737123 and parameters: {'n_estimators': 100, 'max_depth': 11, 'min_samples_split': 17, 'max_features': 'sqrt'}. Best is trial 1 with value: 0.4551641313050441.\n", "[I 2025-04-22 00:46:22,065] Trial 9 finished with value: 0.5129436882839605 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 19, 'max_features': 'sqrt'}. Best is trial 9 with value: 0.5129436882839605.\n", "[I 2025-04-22 00:47:04,255] Trial 10 finished with value: 0.5268214571657326 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 00:47:50,972] Trial 11 finished with value: 0.5268214571657326 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 00:48:37,511] Trial 12 finished with value: 0.5268214571657326 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 13, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 00:49:40,447] Trial 13 finished with value: 0.4454230050707233 and parameters: {'n_estimators': 600, 'max_depth': 9, 'min_samples_split': 12, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 00:50:43,535] Trial 14 finished with value: 0.45116092874299446 and parameters: {'n_estimators': 700, 'max_depth': 8, 'min_samples_split': 9, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 00:52:00,550] Trial 15 finished with value: 0.3975180144115293 and parameters: {'n_estimators': 600, 'max_depth': 13, 'min_samples_split': 14, 'max_features': 'log2'}. Best is trial 10 with value: 0.5268214571657326.\n", "[I 2025-04-22 01:03:17,203] Trial 16 finished with value: 0.5604483586869495 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 10, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 01:22:12,149] Trial 17 finished with value: 0.5238857752868962 and parameters: {'n_estimators': 800, 'max_depth': 9, 'min_samples_split': 9, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 01:50:22,966] Trial 18 finished with value: 0.48919135308246586 and parameters: {'n_estimators': 900, 'max_depth': 13, 'min_samples_split': 6, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:00:00,251] Trial 19 finished with value: 0.4073925807312517 and parameters: {'n_estimators': 500, 'max_depth': 24, 'min_samples_split': 15, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:27:05,051] Trial 20 finished with value: 0.4783827061649319 and parameters: {'n_estimators': 800, 'max_depth': 15, 'min_samples_split': 10, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:27:52,062] Trial 21 finished with value: 0.5272217774219375 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:28:54,157] Trial 22 finished with value: 0.47477982385908724 and parameters: {'n_estimators': 700, 'max_depth': 7, 'min_samples_split': 11, 'max_features': 'log2'}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:52:31,506] Trial 23 finished with value: 0.5188150520416333 and parameters: {'n_estimators': 900, 'max_depth': 10, 'min_samples_split': 7, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:53:24,853] Trial 24 finished with value: 0.48718975180144114 and parameters: {'n_estimators': 600, 'max_depth': 7, 'min_samples_split': 15, 'max_features': 'log2'}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 02:59:10,327] Trial 25 finished with value: 0.5419001868161196 and parameters: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 11, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 03:10:23,810] Trial 26 finished with value: 0.46757405924739787 and parameters: {'n_estimators': 800, 'max_depth': 11, 'min_samples_split': 7, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 03:19:16,275] Trial 27 finished with value: 0.5190819322124366 and parameters: {'n_estimators': 900, 'max_depth': 7, 'min_samples_split': 11, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 03:33:20,468] Trial 28 finished with value: 0.44995996797437954 and parameters: {'n_estimators': 1000, 'max_depth': 11, 'min_samples_split': 8, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 03:46:16,932] Trial 29 finished with value: 0.4316786762743528 and parameters: {'n_estimators': 400, 'max_depth': 23, 'min_samples_split': 2, 'max_features': 0.8}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 03:55:02,373] Trial 30 finished with value: 0.5252201761409127 and parameters: {'n_estimators': 800, 'max_depth': 8, 'min_samples_split': 10, 'max_features': 0.5}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 04:05:13,548] Trial 31 finished with value: 0.5603149186015478 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 12, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 04:13:54,762] Trial 32 finished with value: 0.5600480384307446 and parameters: {'n_estimators': 600, 'max_depth': 5, 'min_samples_split': 12, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 04:23:38,057] Trial 33 finished with value: 0.540565785962103 and parameters: {'n_estimators': 500, 'max_depth': 7, 'min_samples_split': 15, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 04:38:14,404] Trial 34 finished with value: 0.5273552175073392 and parameters: {'n_estimators': 600, 'max_depth': 9, 'min_samples_split': 12, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 04:51:18,405] Trial 35 finished with value: 0.559113957832933 and parameters: {'n_estimators': 900, 'max_depth': 5, 'min_samples_split': 4, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 05:30:48,657] Trial 36 finished with value: 0.4543634907926341 and parameters: {'n_estimators': 1000, 'max_depth': 21, 'min_samples_split': 4, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 05:48:06,955] Trial 37 finished with value: 0.4679743795036029 and parameters: {'n_estimators': 500, 'max_depth': 15, 'min_samples_split': 5, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 06:03:23,812] Trial 38 finished with value: 0.5565785962103015 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 2, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 06:12:11,559] Trial 39 finished with value: 0.5260208166533227 and parameters: {'n_estimators': 400, 'max_depth': 8, 'min_samples_split': 16, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 06:24:37,395] Trial 40 finished with value: 0.46250333600213506 and parameters: {'n_estimators': 300, 'max_depth': 30, 'min_samples_split': 4, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 06:39:50,022] Trial 41 finished with value: 0.5565785962103015 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 2, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 06:55:10,900] Trial 42 finished with value: 0.5565785962103015 and parameters: {'n_estimators': 900, 'max_depth': 6, 'min_samples_split': 2, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 07:31:23,019] Trial 43 finished with value: 0.4528956498532158 and parameters: {'n_estimators': 900, 'max_depth': 27, 'min_samples_split': 4, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 07:43:20,810] Trial 44 finished with value: 0.5435014678409393 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 3, 'max_features': 0.8}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 08:07:55,100] Trial 45 finished with value: 0.5229516946890845 and parameters: {'n_estimators': 800, 'max_depth': 10, 'min_samples_split': 6, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 08:09:27,628] Trial 46 finished with value: 0.5236188951160928 and parameters: {'n_estimators': 700, 'max_depth': 5, 'min_samples_split': 14, 'max_features': 'sqrt'}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 08:28:31,570] Trial 47 finished with value: 0.5254870563117161 and parameters: {'n_estimators': 600, 'max_depth': 8, 'min_samples_split': 3, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 09:23:58,548] Trial 48 finished with value: 0.5483053109153989 and parameters: {'n_estimators': 800, 'max_depth': 7, 'min_samples_split': 12, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n", "[I 2025-04-22 09:50:02,761] Trial 49 finished with value: 0.558713637576728 and parameters: {'n_estimators': 1000, 'max_depth': 6, 'min_samples_split': 8, 'max_features': None}. Best is trial 16 with value: 0.5604483586869495.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "āœ… Best Params for XAUUSD: {'n_estimators': 800, 'max_depth': 5, 'min_samples_split': 10, 'max_features': None}\n", "šŸ’¾ Saved best model for XAUUSD to models/rf_models\\XAUUSD_best_model.pkl\n", "\n", "āœ… All symbols optimized. MT5 connection closed.\n", "\n", "šŸ“„ Saved optimization summary to models/rf_models\\summary_results.csv\n", " Symbol BestParams BestAccuracy\n", "0 US30 {'n_estimators': 200, 'max_depth': 5, 'min_sam... 0.580998\n", "1 US500 {'n_estimators': 800, 'max_depth': 5, 'min_sam... 0.570857\n", "2 DE40 {'n_estimators': 800, 'max_depth': 5, 'min_sam... 0.520541\n", "3 JP225 {'n_estimators': 1000, 'max_depth': 5, 'min_sa... 0.408727\n", "4 UK100 {'n_estimators': 900, 'max_depth': 5, 'min_sam... 0.532714\n", "5 XAUUSD {'n_estimators': 800, 'max_depth': 5, 'min_sam... 0.560448\n" ] } ], "source": [ "# ----------------------------------------------------------------------------\n", "# FINAL PRO VERSION: Multi-Symbol Model Optimization with Optuna + TimeSeriesSplit\n", "# ----------------------------------------------------------------------------\n", "\n", "import sys\n", "import os\n", "import warnings\n", "from pathlib import Path\n", "\n", "# ---------------------------------------------------------------------------\n", "# 0) SETUP\n", "# ---------------------------------------------------------------------------\n", "project_root = Path.cwd().parent.parent\n", "sys.path.append(str(project_root))\n", "os.chdir(str(project_root))\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import MetaTrader5 as mt5\n", "import joblib\n", "import optuna\n", "\n", "from data.data_loader_mt5 import get_data_mt5\n", "from features.feature_engineering import add_all_ta_features\n", "from features.labeling_schemes import create_labels_multi_bar\n", "from sklearn.model_selection import TimeSeriesSplit\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.metrics import accuracy_score\n", "\n", "# ----------------------------------------------------------------------------\n", "# 1) CONFIGURATION\n", "# ----------------------------------------------------------------------------\n", "symbols = [\"US30\", \"US500\", \"DE40\", \"JP225\", \"UK100\", \"XAUUSD\"]\n", "timeframe = mt5.TIMEFRAME_H4\n", "n_bars = 10000\n", "start_pos = 5000\n", "horizon = 5\n", "threshold = 0.005\n", "folds = 3\n", "trials = 50\n", "\n", "save_folder = \"models/rf_models\"\n", "os.makedirs(save_folder, exist_ok=True)\n", "\n", "summary_rows = []\n", "\n", "# ----------------------------------------------------------------------------\n", "# 2) START MULTI-SYMBOL OPTIMIZATION\n", "# ----------------------------------------------------------------------------\n", "print(\"\\nāš” Starting MT5 connection...\")\n", "if not mt5.initialize():\n", " raise RuntimeError(\"āŒ Failed to initialize MT5\")\n", "\n", "for symbol in symbols:\n", " print(f\"\\n\\nšŸ”µ Optimizing Symbol: {symbol}\")\n", "\n", " # Fetch Data\n", " data = get_data_mt5(symbol=symbol, timeframe=timeframe, n_bars=n_bars, start_pos=start_pos)\n", " if data is None or len(data) < 1000:\n", " print(f\"āš ļø Not enough data for {symbol}, skipping...\")\n", " continue\n", "\n", " df = add_all_ta_features(data)\n", " df_lbl = create_labels_multi_bar(df, horizon=horizon, threshold=threshold)\n", "\n", " X = df_lbl.drop(columns=[\"multi_bar_label\", \"future_return_h\"])\n", " y = df_lbl[\"multi_bar_label\"]\n", "\n", " tscv = TimeSeriesSplit(n_splits=folds)\n", "\n", " # Define Optuna Objective\n", " def objective(trial):\n", " params = {\n", " \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000, step=100),\n", " \"max_depth\": trial.suggest_int(\"max_depth\", 5, 30),\n", " \"min_samples_split\": trial.suggest_int(\"min_samples_split\", 2, 20),\n", " \"max_features\": trial.suggest_categorical(\"max_features\", [\"sqrt\", \"log2\", 0.5, 0.8, None])\n", " }\n", "\n", " model = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"rf\", RandomForestClassifier(**params, random_state=42))\n", " ])\n", "\n", " scores = []\n", " for train_idx, test_idx in tscv.split(X):\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]\n", "\n", " model.fit(X_train, y_train + 1)\n", " preds = model.predict(X_test)\n", " scores.append(accuracy_score(y_test + 1, preds))\n", "\n", " return np.mean(scores)\n", "\n", " # Run Optuna\n", " study = optuna.create_study(direction=\"maximize\")\n", " study.optimize(objective, n_trials=trials)\n", "\n", " print(f\"āœ… Best Params for {symbol}: {study.best_params}\")\n", "\n", " # Retrain best model\n", " best_params = study.best_params\n", "\n", " best_model = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"rf\", RandomForestClassifier(\n", " n_estimators=best_params[\"n_estimators\"],\n", " max_depth=best_params[\"max_depth\"],\n", " min_samples_split=best_params[\"min_samples_split\"],\n", " max_features=best_params[\"max_features\"],\n", " random_state=42\n", " ))\n", " ])\n", "\n", " best_model.fit(X, y + 1)\n", "\n", " # Save model\n", " model_path = os.path.join(save_folder, f\"{symbol}_best_model.pkl\")\n", " joblib.dump(best_model, model_path)\n", "\n", " print(f\"šŸ’¾ Saved best model for {symbol} to {model_path}\")\n", "\n", " # Save best performance in summary\n", " summary_rows.append({\n", " \"Symbol\": symbol,\n", " \"BestParams\": study.best_params,\n", " \"BestAccuracy\": study.best_value\n", " })\n", "\n", "mt5.shutdown()\n", "print(\"\\nāœ… All symbols optimized. MT5 connection closed.\")\n", "\n", "# ----------------------------------------------------------------------------\n", "# 3) SAVE SUMMARY TABLE\n", "# ----------------------------------------------------------------------------\n", "summary_df = pd.DataFrame(summary_rows)\n", "summary_path = os.path.join(save_folder, \"summary_results.csv\")\n", "summary_df.to_csv(summary_path, index=False)\n", "\n", "print(f\"\\nšŸ“„ Saved optimization summary to {summary_path}\")\n", "print(summary_df)\n" ] } ], "metadata": { "kernelspec": { "display_name": "ml", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 2 }