{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regime Detection\n",
    "\n",
    "Description:     \n",
    "We detect market regimes (up, down, sideways) using a simple rule—if a short moving average is above a longer one, label = +1 (“up” regime), if below, label = -1 (“down” regime), otherwise 0 (“sideways”). By identifying these regimes, we can adapt trading strategies to different market states or selectively trade only in certain conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 📌 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",
    "✅ 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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of folds created: 3\n",
      "\n",
      "===== Fold 1 =====\n",
      "Train size: 237, Test size: 237\n",
      "Selected features for Fold 1: ['trend_kst_sig', 'trend_macd_signal', 'momentum_ppo_signal', 'trend_visual_ichimoku_b', 'momentum_tsi', 'trend_kst', 'trend_trix', 'trend_aroon_up', 'trend_visual_ichimoku_a', 'trend_macd', 'momentum_ppo', 'momentum_stoch_rsi_k', 'volatility_ui', 'trend_ichimoku_b', 'momentum_stoch_rsi_d', 'trend_ichimoku_conv', 'trend_mass_index', 'trend_adx_neg', 'momentum_wr', 'momentum_stoch_rsi']\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": [
      "[LightGBM] [Info] Number of positive: 166, number of negative: 71\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000173 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 1418\n",
      "[LightGBM] [Info] Number of data points in the train set: 237, number of used features: 20\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.700422 -> initscore=0.849308\n",
      "[LightGBM] [Info] Start training from score 0.849308\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "===== Fold 2 =====\n",
      "Train size: 474, Test size: 237\n",
      "Selected features for Fold 2: ['trend_kst_sig', 'trend_macd_signal', 'momentum_ppo_signal', 'trend_trix', 'trend_kst', 'momentum_tsi', 'momentum_ppo', 'trend_macd', 'momentum_stoch_rsi', 'momentum_ppo_hist', 'momentum_stoch_rsi_k', 'trend_macd_diff', 'trend_visual_ichimoku_a', 'trend_kst_diff', 'momentum_stoch_rsi_d', 'volume_nvi', 'trend_aroon_up', 'trend_aroon_ind', 'trend_aroon_down', 'trend_adx_neg']\n",
      "[LightGBM] [Info] Number of positive: 347, number of negative: 127\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000195 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2730\n",
      "[LightGBM] [Info] Number of data points in the train set: 474, number of used features: 20\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.732068 -> initscore=1.005138\n",
      "[LightGBM] [Info] Start training from score 1.005138\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "===== Fold 3 =====\n",
      "Train size: 711, Test size: 237\n",
      "Selected features for Fold 3: ['trend_kst_sig', 'trend_macd_signal', 'momentum_ppo_signal', 'trend_kst', 'trend_trix', 'momentum_tsi', 'momentum_ppo', 'trend_macd', 'momentum_stoch_rsi_k', 'momentum_ppo_hist', 'trend_aroon_up', 'trend_macd_diff', 'momentum_stoch_rsi', 'momentum_stoch_rsi_d', 'volatility_ui', 'trend_aroon_down', 'trend_kst_diff', 'momentum_rsi', 'momentum_ao', 'trend_stc']\n",
      "[LightGBM] [Info] Number of positive: 470, number of negative: 241\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 4326\n",
      "[LightGBM] [Info] Number of data points in the train set: 711, number of used features: 20\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.661041 -> initscore=0.667936\n",
      "[LightGBM] [Info] Start training from score 0.667936\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "=== Fold 1 Results ===\n",
      "RandomForestClassifier: ACC=0.95, Return=-2.82%, Sharpe=-0.00\n",
      "GradientBoostingClassifier: ACC=0.97, Return=-6.91%, Sharpe=-0.02\n",
      "SVC: ACC=0.89, Return=14.37%, Sharpe=0.06\n",
      "XGBClassifier: ACC=0.96, Return=-3.55%, Sharpe=-0.01\n",
      "LGBMClassifier: ACC=0.97, Return=-8.13%, Sharpe=-0.03\n",
      "\n",
      "=== Fold 2 Results ===\n",
      "RandomForestClassifier: ACC=0.95, Return=-25.27%, Sharpe=-0.11\n",
      "GradientBoostingClassifier: ACC=0.97, Return=-18.73%, Sharpe=-0.08\n",
      "SVC: ACC=0.92, Return=-15.33%, Sharpe=-0.06\n",
      "XGBClassifier: ACC=0.97, Return=-22.06%, Sharpe=-0.09\n",
      "LGBMClassifier: ACC=0.97, Return=-22.06%, Sharpe=-0.09\n",
      "\n",
      "=== Fold 3 Results ===\n",
      "RandomForestClassifier: ACC=0.92, Return=-14.90%, Sharpe=-0.05\n",
      "GradientBoostingClassifier: ACC=0.86, Return=-16.71%, Sharpe=-0.06\n",
      "SVC: ACC=0.91, Return=-30.49%, Sharpe=-0.13\n",
      "XGBClassifier: ACC=0.89, Return=-8.63%, Sharpe=-0.03\n",
      "LGBMClassifier: ACC=0.89, Return=-14.26%, Sharpe=-0.05\n"
     ]
    }
   ],
   "source": [
    "# REGIME DETECTION + LabelEncoder approach for SHIFTED labels\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "# ---------------------------------------------------------------------------\n",
    "# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
    "# ---------------------------------------------------------------------------\n",
    "project_root = Path.cwd().parent.parent  # Adjust if your notebook is in notebooks/time_series\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",
    "\n",
    "# Our modules\n",
    "from data.data_loader import get_data_mt5\n",
    "from features.feature_engineering import add_all_ta_features\n",
    "from models.model_training import (\n",
    "    select_features_rf_reg,\n",
    "    walk_forward_splits\n",
    ")\n",
    "from backtests.simple_backtest import simulate_trading, calculate_sharpe_ratio\n",
    "from features.labeling_schemes import create_labels_regime_detection  # or define inline\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Sklearn / Models\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\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",
    "\n",
    "\n",
    "###########################################################\n",
    "# 1) DATA LOADING & FEATURE ENGINEERING\n",
    "###########################################################\n",
    "if not mt5.initialize():\n",
    "    print(\"Failed to initialize MT5\")\n",
    "else:\n",
    "    data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=2000, start_pos=2000)\n",
    "    mt5.shutdown()\n",
    "\n",
    "df = add_all_ta_features(data)\n",
    "\n",
    "###########################################################\n",
    "# 2) Regime Detection Labeling\n",
    "###########################################################\n",
    "\n",
    "\n",
    "df_lbl = create_labels_regime_detection(df, short_window=20, long_window=50)\n",
    "\n",
    "# X => all features except the label columns\n",
    "X = df_lbl.drop(columns=[\"regime_label\", \"ma_short\", \"ma_long\"])\n",
    "y = df_lbl[\"regime_label\"]  # in {-1,0,+1}\n",
    "\n",
    "###########################################################\n",
    "# SHIFT LABELS? \n",
    "# We'll SHIFT them to [0,1,2] in each fold with LabelEncoder\n",
    "# so we can handle folds with only two classes, e.g. {0,2}.\n",
    "###########################################################\n",
    "\n",
    "###########################################################\n",
    "# 3) WALK-FORWARD SPLITS\n",
    "###########################################################\n",
    "folds = walk_forward_splits(X, y, n_splits=3)\n",
    "print(f\"Number of folds created: {len(folds)}\")\n",
    "\n",
    "###########################################################\n",
    "# 4) DEFINE CLASSIFICATION MODELS\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(),  # <-- Added Bayesian Classification Model\n",
    "    \"BernoulliNB\": BernoulliNB() # <-- Another Bayesian Model (good for binary data)\n",
    "}\n",
    "\n",
    "###########################################################\n",
    "# 5) LOOP OVER FOLDS + SIMPLE BACKTEST (with LabelEncoder)\n",
    "###########################################################\n",
    "fold_results = {}\n",
    "\n",
    "for fold_i, (X_train_fold, y_train_fold, X_test_fold, y_test_fold) in enumerate(folds, start=1):\n",
    "    print(f\"\\n===== Fold {fold_i} =====\")\n",
    "    print(f\"Train size: {len(X_train_fold)}, Test size: {len(X_test_fold)}\")\n",
    "    \n",
    "    # 5.1 SHIFT labels with LabelEncoder so classes become consecutive integers\n",
    "    le = LabelEncoder()\n",
    "    \n",
    "    # Fit on the training labels (which might be e.g. {-1,+1}, => SHIFT with le)\n",
    "    # or {0,1} => SHIFT, etc. This ensures e.g. {0,2} becomes {0,1}.\n",
    "    le.fit(y_train_fold)\n",
    "    y_train_enc = le.transform(y_train_fold)\n",
    "    \n",
    "    # 5.2 Feature selection\n",
    "    # We can pass the encoded y to select_features_rf_reg\n",
    "    # because it just needs an estimator with .fit(X, y).\n",
    "    # SHIFT isn't strictly necessary for random forest's .feature_importances_,\n",
    "    # but let's be consistent.\n",
    "    rf_for_fs = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "    X_train_sel, selected_idx = select_features_rf_reg(\n",
    "        X_train_fold, y_train_enc, estimator=rf_for_fs, max_features=20\n",
    "    )\n",
    "    feats = X_train_fold.columns[selected_idx]\n",
    "    print(f\"Selected features for Fold {fold_i}: {feats.tolist()}\")\n",
    "\n",
    "    X_test_sel = X_test_fold[feats]\n",
    "\n",
    "    # 5.3 Scale\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train_sel)\n",
    "    X_test_scaled  = scaler.transform(X_test_sel)\n",
    "\n",
    "    fold_results[fold_i] = {}\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        # 5.4 Train on the encoded training labels\n",
    "        model.fit(X_train_scaled, y_train_enc)\n",
    "\n",
    "        # 5.5 Predict encoded classes on test\n",
    "        preds_enc = model.predict(X_test_scaled)\n",
    "        \n",
    "        # 5.6 Decode them back to the original label set (which might be e.g. [-1,0,+1])\n",
    "        preds_fold = le.inverse_transform(preds_enc)\n",
    "\n",
    "        # 5.7 Evaluate accuracy on the unencoded test labels\n",
    "        acc = accuracy_score(y_test_fold, preds_fold)\n",
    "\n",
    "        # 5.8 Convert classification => signals\n",
    "        # If your original labels are: +1 => up, -1 => down, 0 => sideways,\n",
    "        # then we interpret them as: +1 => buy, -1 => sell, 0 => no trade\n",
    "        signals = preds_fold\n",
    "\n",
    "        # Align with test portion\n",
    "        df_test_fold = df_lbl.loc[X_test_fold.index].copy()\n",
    "\n",
    "        # 5.9 Simple backtest with cost\n",
    "        daily_returns, total_return = simulate_trading(signals, df_test_fold, cost=0.0002)\n",
    "        sr = calculate_sharpe_ratio(np.array(daily_returns))\n",
    "\n",
    "        fold_results[fold_i][model_name] = {\n",
    "            \"Accuracy\": acc,\n",
    "            \"TotalReturn\": total_return,\n",
    "            \"Sharpe\": sr\n",
    "        }\n",
    "\n",
    "###########################################################\n",
    "# 6) PRINT RESULTS\n",
    "###########################################################\n",
    "for fold_i, model_dict in fold_results.items():\n",
    "    print(f\"\\n=== Fold {fold_i} Results ===\")\n",
    "    for model_name, stats in model_dict.items():\n",
    "        acc = stats[\"Accuracy\"]\n",
    "        ret = stats[\"TotalReturn\"]\n",
    "        sr = stats[\"Sharpe\"]\n",
    "        print(f\"{model_name}: ACC={acc:.2f}, Return={ret:.2f}%, Sharpe={sr:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: 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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique classes in y_full: [-1  1]\n",
      "Unique classes in y_full_shifted: [0 2]\n",
      "Tuning portion size: 1560\n",
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "Best params: {'clf__n_estimators': 200, 'clf__min_samples_split': 5, 'clf__max_features': 'sqrt', 'clf__max_depth': 10}\n",
      "Best score (accuracy): 0.9290598290598292\n",
      "Saved best estimator to 'best_rf_rd_pipeline.pkl'\n"
     ]
    }
   ],
   "source": [
    "# Code 2: Hyperparameter Tuning for a Classification Model (Regime Detection)\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "# ---------------------------------------------------------------------------\n",
    "# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
    "# ---------------------------------------------------------------------------\n",
    "project_root = Path.cwd().parent.parent  # Adjust if your notebook is in notebooks/time_series\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",
    "\n",
    "# Sklearn / Models\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import TimeSeriesSplit, RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score, make_scorer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Your modules\n",
    "from data.data_loader import get_data_mt5\n",
    "from features.feature_engineering import add_all_ta_features\n",
    "from features.labeling_schemes import create_labels_regime_detection  # or define inline\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "###########################################################\n",
    "# 1) DATA LOADING & FEATURE ENGINEERING\n",
    "###########################################################\n",
    "if not mt5.initialize():\n",
    "    print(\"Failed to initialize MT5\")\n",
    "else:\n",
    "    data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=2000, start_pos=2000)\n",
    "    mt5.shutdown()\n",
    "\n",
    "df = add_all_ta_features(data)\n",
    "\n",
    "###########################################################\n",
    "# 2) Regime Detection Labeling\n",
    "###########################################################\n",
    "\n",
    "# Create classification labels for regime detection\n",
    "df_lbl = create_labels_regime_detection(df, short_window=20, long_window=50)\n",
    "\n",
    "# Now we have columns: 'ma_short', 'ma_long', 'regime_label' in {-1,0,+1}\n",
    "X_full = df_lbl.drop(columns=[\"regime_label\", \"ma_short\", \"ma_long\"])\n",
    "y_full = df_lbl[\"regime_label\"]\n",
    "\n",
    "# SHIFT LABELS from [-1,0,+1] => [0,1,2]\n",
    "# so the classifier won't complain about negative labels\n",
    "y_full_shifted = y_full + 1  # -1->0, 0->1, +1->2\n",
    "\n",
    "print(\"Unique classes in y_full:\", y_full.unique())\n",
    "print(\"Unique classes in y_full_shifted:\", y_full_shifted.unique())\n",
    "\n",
    "# Ensure chronological order if needed\n",
    "# X_full = X_full.sort_index()\n",
    "# y_full_shifted = y_full_shifted.loc[X_full.index]\n",
    "\n",
    "###########################################################\n",
    "# 3) DEFINE YOUR TRAIN PORTION\n",
    "###########################################################\n",
    "# e.g., first 80% for hyperparam tuning\n",
    "split_idx = int(len(X_full) * 0.8)\n",
    "X_tune = X_full.iloc[:split_idx].copy()\n",
    "y_tune_shifted = y_full_shifted.iloc[:split_idx].copy()\n",
    "\n",
    "print(f\"Tuning portion size: {len(X_tune)}\")\n",
    "\n",
    "###########################################################\n",
    "# 4) TIME-BASED CV (TimeSeriesSplit)\n",
    "###########################################################\n",
    "tscv = TimeSeriesSplit(n_splits=3)\n",
    "\n",
    "# We'll define a scoring for classification (accuracy)\n",
    "scorer = make_scorer(accuracy_score)\n",
    "\n",
    "###########################################################\n",
    "# 5) BUILD A PIPELINE\n",
    "###########################################################\n",
    "pipeline = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"clf\", RandomForestClassifier(random_state=42))\n",
    "])\n",
    "\n",
    "###########################################################\n",
    "# 6) DEFINE PARAM DISTRIBUTIONS FOR RandomForestClassifier\n",
    "###########################################################\n",
    "param_distributions = {\n",
    "    \"clf__n_estimators\": [100, 200, 300],\n",
    "    \"clf__max_depth\": [None, 5, 10, 15],\n",
    "    \"clf__min_samples_split\": [2, 5, 10],\n",
    "    \"clf__max_features\": [\"auto\", \"sqrt\", 0.5]\n",
    "}\n",
    "\n",
    "###########################################################\n",
    "# 7) SET UP RandomizedSearchCV\n",
    "###########################################################\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_distributions=param_distributions,\n",
    "    n_iter=10,               # how many random combos\n",
    "    scoring=scorer,          # 'accuracy' metric\n",
    "    cv=tscv,                 # time-based folds\n",
    "    random_state=42,\n",
    "    n_jobs=-1,\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "###########################################################\n",
    "# 8) FIT ON TUNING PORTION\n",
    "###########################################################\n",
    "random_search.fit(X_tune, y_tune_shifted)\n",
    "\n",
    "print(\"Best params:\", random_search.best_params_)\n",
    "print(\"Best score (accuracy):\", random_search.best_score_)\n",
    "\n",
    "best_estimator = random_search.best_estimator_\n",
    "\n",
    "###########################################################\n",
    "# 9) SAVE THE BEST ESTIMATOR\n",
    "###########################################################\n",
    "joblib.dump(best_estimator, \"models/saved_models/best_rf_rd_pipeline.pkl\")\n",
    "print(\"Saved best estimator to 'best_rf_rd_pipeline.pkl'\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: 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 “live” 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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded best classification model from 'best_rf_rd_pipeline.pkl'\n",
      "\n",
      "Out-of-Sample Accuracy: 0.9821\n",
      "\n",
      "Running Full Backtest on the Last 2000 Bars...\n",
      "\n",
      "Full Backtest Results:\n",
      "Accuracy=0.98, Return=-0.17%, Sharpe=-0.39\n",
      "Start                                2024-04-11 16:00:00\n",
      "End                                  2025-03-02 16:00:00\n",
      "Period                                 325 days 04:00:00\n",
      "Start Value                                      10000.0\n",
      "End Value                                     8340.15852\n",
      "Total Return [%]                              -16.598415\n",
      "Benchmark Return [%]                           21.623307\n",
      "Max Gross Exposure [%]                             100.0\n",
      "Total Fees Paid                                94.120141\n",
      "Max Drawdown [%]                               29.083683\n",
      "Max Drawdown Duration                  213 days 12:00:00\n",
      "Total Trades                                          25\n",
      "Total Closed Trades                                   25\n",
      "Total Open Trades                                      0\n",
      "Open Trade PnL                                       0.0\n",
      "Win Rate [%]                                        40.0\n",
      "Best Trade [%]                                 22.932124\n",
      "Worst Trade [%]                                -9.560755\n",
      "Avg Winning Trade [%]                           5.949059\n",
      "Avg Losing Trade [%]                           -4.782133\n",
      "Avg Winning Trade Duration    11 days 14:23:59.999999999\n",
      "Avg Losing Trade Duration                3 days 21:20:00\n",
      "Profit Factor                                   0.760015\n",
      "Expectancy                                    -66.393659\n",
      "Sharpe Ratio                                   -0.389969\n",
      "Calmar Ratio                                   -0.633769\n",
      "Omega Ratio                                     0.966828\n",
      "Sortino Ratio                                   -0.54345\n",
      "dtype: object\n"
     ]
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   "source": [
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "# ---------------------------------------------------------------------------\n",
    "# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\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",
    "\n",
    "# Our modules\n",
    "from data.data_loader import get_data_mt5\n",
    "from features.feature_engineering import add_all_ta_features\n",
    "from features.labeling_schemes import create_labels_regime_detection\n",
    "\n",
    "# Sklearn\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "###########################################################\n",
    "# 1) DATA LOADING & FEATURE ENGINEERING\n",
    "###########################################################\n",
    "if not mt5.initialize():\n",
    "    print(\"Failed to initialize MT5\")\n",
    "else:\n",
    "    # Fetch 2000 most recent bars for backtesting\n",
    "    data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=2000, start_pos=0)\n",
    "    mt5.shutdown()\n",
    "\n",
    "# Add technical features\n",
    "df = add_all_ta_features(data)\n",
    "\n",
    "# Create regime detection labels\n",
    "df_lbl = create_labels_regime_detection(df, short_window=20, long_window=50)\n",
    "\n",
    "# Separate features and labels\n",
    "X = df_lbl.drop(columns=[\"regime_label\", \"ma_short\", \"ma_long\"])\n",
    "y = df_lbl[\"regime_label\"]          # in {-1, 0, +1}\n",
    "y_shifted = y + 1                    # in {0,1,2} if your classifier used shifted labels\n",
    "\n",
    "###########################################################\n",
    "# 2) LOAD PRE-TRAINED CLASSIFICATION MODEL (NO RETRAINING)\n",
    "###########################################################\n",
    "best_pipeline = joblib.load(\"models/saved_models/best_rf_rd_pipeline.pkl\")\n",
    "print(\"Loaded best classification model from 'best_rf_rd_pipeline.pkl'\")\n",
    "\n",
    "# Identify the columns the pipeline was trained on\n",
    "trained_columns = best_pipeline[\"scaler\"].feature_names_in_  # Adjust if your pipeline step name differs\n",
    "\n",
    "# Subset X to match these columns\n",
    "X_test = X[trained_columns]\n",
    "\n",
    "# Predict on the new dataset\n",
    "preds_shifted = best_pipeline.predict(X_test)\n",
    "\n",
    "# Shift predictions back to {-1, 0, +1}\n",
    "preds = preds_shifted - 1\n",
    "\n",
    "# Accuracy on the exact prediction rows\n",
    "accuracy = accuracy_score(y.loc[X_test.index], preds)\n",
    "print(f\"\\nOut-of-Sample Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "###########################################################\n",
    "# 3) BACKTEST via target exposure (-1, 0, +1)  << UPDATED\n",
    "###########################################################\n",
    "# predictions already in {-1, 0, +1}\n",
    "exposure = pd.Series(preds.astype(float), index=X_test.index)\n",
    "\n",
    "# Align prices exactly to prediction rows (no padding)\n",
    "close = df_lbl.loc[X_test.index, \"close\"]\n",
    "\n",
    "# Optional: trade on next bar to avoid look-ahead (set to 0 for same-bar)\n",
    "execution_lag = 1\n",
    "if execution_lag > 0:\n",
    "    exposure = exposure.shift(execution_lag).fillna(0.0)\n",
    "\n",
    "fees = 0.0002  # 0.02% transaction cost per trade\n",
    "\n",
    "pf = vbt.Portfolio.from_orders(\n",
    "    close=close,\n",
    "    size=exposure,              # -1 short, 0 flat, +1 long\n",
    "    size_type='targetpercent',\n",
    "    init_cash=10000,\n",
    "    freq='4H',\n",
    "    fees=fees\n",
    ")\n",
    "\n",
    "total_return = pf.total_return()\n",
    "sharpe_ratio = pf.sharpe_ratio()\n",
    "\n",
    "print(\"\\nFull Backtest Results:\")\n",
    "print(f\"Accuracy={accuracy:.4f}, Return={total_return:.2f}%, Sharpe={sharpe_ratio:.2f}\")\n",
    "print(pf.stats())\n",
    "\n",
    "# Optional: Plot the backtest\n",
    "fig = pf.plot()\n",
    "fig.show()\n"
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