loo on features

Implemented Supertrend indicators for feature engineering in main.py, including caching of computed features. Updated plotting functions in plot_results.py to save charts in a dedicated directory and added new functions for directional accuracy and prediction transition heatmaps.
Added multiple technical indicators for feature engineering, including ADX, TRIX, Vortex, KAMA, Force Index, EOM, MFI, ADI, TEMA, StochRSI, and Awesome Oscillator. Improved NaN handling and implemented leave-one-out feature evaluation with results saved to CSV.
2025-05-30 20:06:28 +08:00 · 2025-05-30 18:14:42 +08:00 · 2025-05-30 17:59:09 +08:00 · 2025-05-30 15:54:48 +08:00 · 2025-05-30 15:54:14 +08:00 · 2025-05-30 15:36:43 +08:00
12 changed files with 1898 additions and 428 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,11 +1,13 @@
 # ---> Python
-*.json
+/data/*.db
+/credentials/*.json
 *.csv
 *.png
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
+/data/*.npy

 # C extensions
 *.so
--- a/.python-version
+++ b/.python-version
@@ -1 +0,0 @@
-3.10
--- a/cycles/backtest.py
+++ b/cycles/backtest.py
@@ -1,5 +1,6 @@
 import pandas as pd
 import numpy as np
+import time

 from cycles.supertrend import Supertrends
 from cycles.market_fees import MarketFees
@@ -27,6 +28,9 @@ class Backtest:
        trends_arr = np.stack(trends, axis=1)
        meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
                                trends_arr[:,0], 0)
+        # Shift meta_trend by one to avoid lookahead bias
+        meta_trend_signal = np.roll(meta_trend, 1)
+        meta_trend_signal[0] = 0  # or np.nan, but 0 means 'no signal' for first bar
        
        position = 0  # 0 = no position, 1 = long
        entry_price = 0
@@ -39,14 +43,22 @@ class Backtest:
        entry_time = None
        current_trade_min1_start_idx = None

-        min1_df['timestamp'] = pd.to_datetime(min1_df.index)
+        min1_df.index = pd.to_datetime(min1_df.index)
+        min1_timestamps = min1_df.index.values

+        last_print_time = time.time()
        for i in range(1, len(_df)):
+            current_time = time.time()
+            if current_time - last_print_time >= 5:
+                progress = (i / len(_df)) * 100
+                print(f"\rProgress: {progress:.1f}%", end="", flush=True)
+                last_print_time = current_time
+
            price_open = _df['open'].iloc[i]
            price_close = _df['close'].iloc[i]
            date = _df['timestamp'].iloc[i]
-            prev_mt = meta_trend[i-1]
-            curr_mt = meta_trend[i]
+            prev_mt = meta_trend_signal[i-1]
+            curr_mt = meta_trend_signal[i]
            
            # Check stop loss if in position
            if position == 1:
@@ -87,6 +99,8 @@ class Backtest:
            drawdown = (max_balance - balance) / max_balance
            drawdowns.append(drawdown)

+        print("\rProgress: 100%\r\n", end="", flush=True)
+
        # If still in position at end, sell at last close
        if position == 1:
            exit_result = Backtest.handle_exit(coin, _df['close'].iloc[-1], entry_price, entry_time, _df['timestamp'].iloc[-1])
--- a/cycles/supertrend.py
+++ b/cycles/supertrend.py
@@ -1,70 +1,30 @@
 import pandas as pd
 import numpy as np
 import logging
-from scipy.signal import find_peaks
-from matplotlib.patches import Rectangle
-from scipy import stats
-import concurrent.futures
-from functools import partial
 from functools import lru_cache
-import matplotlib.pyplot as plt

-# Color configuration
-# Plot colors
-DARK_BG_COLOR = '#181C27'
-LEGEND_BG_COLOR = '#333333'
-TITLE_COLOR = 'white'
-AXIS_LABEL_COLOR = 'white'
-
-# Candlestick colors
-CANDLE_UP_COLOR = '#089981'  # Green
-CANDLE_DOWN_COLOR = '#F23645'  # Red
-
-# Marker colors
-MIN_COLOR = 'red'
-MAX_COLOR = 'green'
-
-# Line style colors
-MIN_LINE_STYLE = 'g--'  # Green dashed
-MAX_LINE_STYLE = 'r--'  # Red dashed
-SMA7_LINE_STYLE = 'y-'  # Yellow solid
-SMA15_LINE_STYLE = 'm-'  # Magenta solid
-
-# SuperTrend colors
-ST_COLOR_UP = 'g-'
-ST_COLOR_DOWN = 'r-'
-
-# Cache the calculation results by function parameters
@lru_cache(maxsize=32)
 def cached_supertrend_calculation(period, multiplier, data_tuple):
-    # Convert tuple back to numpy arrays
    high = np.array(data_tuple[0])
    low = np.array(data_tuple[1])
    close = np.array(data_tuple[2])
-    
-    # Calculate TR and ATR using vectorized operations
    tr = np.zeros_like(close)
    tr[0] = high[0] - low[0]
    hc_range = np.abs(high[1:] - close[:-1])
    lc_range = np.abs(low[1:] - close[:-1])
    hl_range = high[1:] - low[1:]
    tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
-    
-    # Use numpy's exponential moving average
    atr = np.zeros_like(tr)
    atr[0] = tr[0]
    multiplier_ema = 2.0 / (period + 1)
    for i in range(1, len(tr)):
        atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
-
-    # Calculate bands
    upper_band = np.zeros_like(close)
    lower_band = np.zeros_like(close)
    for i in range(len(close)):
        hl_avg = (high[i] + low[i]) / 2
        upper_band[i] = hl_avg + (multiplier * atr[i])
        lower_band[i] = hl_avg - (multiplier * atr[i])
-
    final_upper = np.zeros_like(close)
    final_lower = np.zeros_like(close)
    supertrend = np.zeros_like(close)
@@ -106,76 +66,18 @@ def cached_supertrend_calculation(period, multiplier, data_tuple):
    }

 def calculate_supertrend_external(data, period, multiplier):
-    # Convert DataFrame columns to hashable tuples
    high_tuple = tuple(data['high'])
    low_tuple = tuple(data['low'])
    close_tuple = tuple(data['close'])
-    
-    # Call the cached function
    return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))

-
 class Supertrends:
    def __init__(self, data, verbose=False, display=False):
-        """
-        Initialize the TrendDetectorSimple class.
-        
-        Parameters:
-        - data: pandas DataFrame containing price data
-        - verbose: boolean, whether to display detailed logging information
-        - display: boolean, whether to enable display/plotting features
-        """
-        
        self.data = data
        self.verbose = verbose
-        self.display = display
-        
-        # Only define display-related variables if display is True
-        if self.display:
-            # Plot style configuration
-            self.plot_style = 'dark_background' 
-            self.bg_color = DARK_BG_COLOR
-            self.plot_size = (12, 8)
-            
-            # Candlestick configuration
-            self.candle_width = 0.6
-            self.candle_up_color = CANDLE_UP_COLOR
-            self.candle_down_color = CANDLE_DOWN_COLOR
-            self.candle_alpha = 0.8
-            self.wick_width = 1
-            
-            # Marker configuration
-            self.min_marker = '^'
-            self.min_color = MIN_COLOR
-            self.min_size = 100
-            self.max_marker = 'v'
-            self.max_color = MAX_COLOR
-            self.max_size = 100
-            self.marker_zorder = 100
-            
-            # Line configuration
-            self.line_width = 1
-            self.min_line_style = MIN_LINE_STYLE
-            self.max_line_style = MAX_LINE_STYLE
-            self.sma7_line_style = SMA7_LINE_STYLE
-            self.sma15_line_style = SMA15_LINE_STYLE
-            
-            # Text configuration
-            self.title_size = 14
-            self.title_color = TITLE_COLOR
-            self.axis_label_size = 12
-            self.axis_label_color = AXIS_LABEL_COLOR
-            
-            # Legend configuration
-            self.legend_loc = 'best'
-            self.legend_bg_color = LEGEND_BG_COLOR
-        
-        # Configure logging
        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
                           format='%(asctime)s - %(levelname)s - %(message)s')
        self.logger = logging.getLogger('TrendDetectorSimple')
-        
-        # Convert data to pandas DataFrame if it's not already
        if not isinstance(self.data, pd.DataFrame):
            if isinstance(self.data, list):
                self.data = pd.DataFrame({'close': self.data})
@@ -183,154 +85,101 @@ class Supertrends:
                raise ValueError("Data must be a pandas DataFrame or a list")

    def calculate_tr(self):
+        df = self.data.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        tr = np.zeros_like(close)
+        tr[0] = high[0] - low[0]
+        for i in range(1, len(close)):
+            hl_range = high[i] - low[i]
+            hc_range = abs(high[i] - close[i-1])
+            lc_range = abs(low[i] - close[i-1])
+            tr[i] = max(hl_range, hc_range, lc_range)
+        return tr
+
+    def calculate_atr(self, period=14):
+        tr = self.calculate_tr()
+        atr = np.zeros_like(tr)
+        atr[0] = tr[0]
+        multiplier = 2.0 / (period + 1)
+        for i in range(1, len(tr)):
+            atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
+        return atr
+
+    def calculate_supertrend(self, period=10, multiplier=3.0):
        """
-        Calculate True Range (TR) for the price data.
-        
-        True Range is the greatest of:
-        1. Current high - current low
-        2. |Current high - previous close|
-        3. |Current low - previous close|
-        
+        Calculate SuperTrend indicator for the price data.
+        SuperTrend is a trend-following indicator that uses ATR to determine the trend direction.
+        Parameters:
+        - period: int, the period for the ATR calculation (default: 10)
+        - multiplier: float, the multiplier for the ATR (default: 3.0)
        Returns:
-        - Numpy array of TR values
+        - Dictionary containing SuperTrend values, trend direction, and upper/lower bands
        """
        df = self.data.copy()
        high = df['high'].values
        low = df['low'].values
        close = df['close'].values
-        
-        tr = np.zeros_like(close)
-        tr[0] = high[0] - low[0]  # First TR is just the first day's range
-        
+        atr = self.calculate_atr(period)
+        upper_band = np.zeros_like(close)
+        lower_band = np.zeros_like(close)
+        for i in range(len(close)):
+            hl_avg = (high[i] + low[i]) / 2
+            upper_band[i] = hl_avg + (multiplier * atr[i])
+            lower_band[i] = hl_avg - (multiplier * atr[i])
+        final_upper = np.zeros_like(close)
+        final_lower = np.zeros_like(close)
+        supertrend = np.zeros_like(close)
+        trend = np.zeros_like(close)
+        final_upper[0] = upper_band[0]
+        final_lower[0] = lower_band[0]
+        if close[0] <= upper_band[0]:
+            supertrend[0] = upper_band[0]
+            trend[0] = -1
+        else:
+            supertrend[0] = lower_band[0]
+            trend[0] = 1
        for i in range(1, len(close)):
-            # Current high - current low
-            hl_range = high[i] - low[i]
-            # |Current high - previous close|
-            hc_range = abs(high[i] - close[i-1])
-            # |Current low - previous close|
-            lc_range = abs(low[i] - close[i-1])
-            
-            # TR is the maximum of these three values
-            tr[i] = max(hl_range, hc_range, lc_range)
-            
-        return tr
-    
-    def calculate_atr(self, period=14):
-        """
-        Calculate Average True Range (ATR) for the price data.
-        
-        ATR is the exponential moving average of the True Range over a specified period.
-        
-        Parameters:
-        - period: int, the period for the ATR calculation (default: 14)
-        
-        Returns:
-        - Numpy array of ATR values
-        """
-        
-        tr = self.calculate_tr()
-        atr = np.zeros_like(tr)
-        
-        # First ATR value is just the first TR
-        atr[0] = tr[0]
-        
-        # Calculate exponential moving average (EMA) of TR
-        multiplier = 2.0 / (period + 1)
-        
-        for i in range(1, len(tr)):
-            atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
-            
-        return atr
-    
-    def detect_trends(self):
-        """
-        Detect trends by identifying local minima and maxima in the price data
-        using scipy.signal.find_peaks.
-        
-        Parameters:
-        - prominence: float, required prominence of peaks (relative to the price range)
-        - width: int, required width of peaks in data points
-        
-        Returns:
-        - DataFrame with columns for timestamps, prices, and trend indicators
-        - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
-        """
-        df = self.data
-        # close_prices = df['close'].values
-        
-        # max_peaks, _ = find_peaks(close_prices)
-        # min_peaks, _ = find_peaks(-close_prices)
-        
-        # df['is_min'] = False
-        # df['is_max'] = False
-        
-        # for peak in max_peaks:
-        #     df.at[peak, 'is_max'] = True
-        # for peak in min_peaks:
-        #     df.at[peak, 'is_min'] = True
-        
-        # result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
-        
-        # Perform linear regression on min_peaks and max_peaks
-        # min_prices = df['close'].iloc[min_peaks].values
-        # max_prices = df['close'].iloc[max_peaks].values
-        
-        # Linear regression for min peaks if we have at least 2 points
-        # min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
-        # Linear regression for max peaks if we have at least 2 points
-        # max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
-
-        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods        
-        # sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
-        # sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
-        
-        analysis_results = {}
-        # analysis_results['linear_regression'] = {
-        #     'min': {
-        #         'slope': min_slope,
-        #         'intercept': min_intercept,
-        #         'r_squared': min_r_value ** 2
-        #     },
-        #     'max': {
-        #         'slope': max_slope,
-        #         'intercept': max_intercept,
-        #         'r_squared': max_r_value ** 2
-        #     }
-        # }
-        # analysis_results['sma'] = {
-        #     '7': sma_7,
-        #     '15': sma_15
-        # }
-        
-        # Calculate SuperTrend indicators
-        supertrend_results_list = self._calculate_supertrend_indicators()
-        analysis_results['supertrend'] = supertrend_results_list
-        
-        return analysis_results
+            if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
+                final_upper[i] = upper_band[i]
+            else:
+                final_upper[i] = final_upper[i-1]
+            if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
+                final_lower[i] = lower_band[i]
+            else:
+                final_lower[i] = final_lower[i-1]
+            if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+            elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+        supertrend_results = {
+            'supertrend': supertrend,
+            'trend': trend,
+            'upper_band': final_upper,
+            'lower_band': final_lower
+        }
+        return supertrend_results

    def calculate_supertrend_indicators(self):
-        """
-        Calculate SuperTrend indicators with different parameter sets in parallel.
-        Returns:
-        - list, the SuperTrend results
-        """
        supertrend_params = [
-            {"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
+            {"period": 12, "multiplier": 3.0},
+            {"period": 10, "multiplier": 1.0},
+            {"period": 11, "multiplier": 2.0}
        ]
-        data = self.data.copy()
-        
-        # For just 3 calculations, direct calculation might be faster than process pool
        results = []
        for p in supertrend_params:
-            result = calculate_supertrend_external(data, p["period"], p["multiplier"])
-            results.append(result)
-        
-        supertrend_results_list = []
-        for params, result in zip(supertrend_params, results):
-            supertrend_results_list.append({
+            result = self.calculate_supertrend(period=p["period"], multiplier=p["multiplier"])
+            results.append({
                "results": result,
-                "params": params
+                "params": p
            })
-        return supertrend_results_list
+        return results
--- a/data/xgboost_model.json
+++ b/data/xgboost_model.json
--- a/main.py
+++ b/main.py
@@ -6,7 +6,6 @@ import os
 import datetime
 import argparse
 import json
-import ast

 from cycles.utils.storage import Storage
 from cycles.utils.system import SystemUtils
@@ -48,6 +47,7 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
        cumulative_profit = 0
        max_drawdown = 0
        peak = 0
+
        for trade in trades:
            cumulative_profit += trade['profit_pct']
            if cumulative_profit > peak:
@@ -55,10 +55,14 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
            drawdown = peak - cumulative_profit
            if drawdown > max_drawdown:
                max_drawdown = drawdown
+
        final_usd = initial_usd
+
        for trade in trades:
            final_usd *= (1 + trade['profit_pct'])
-        total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
+
+        total_fees_usd = sum(trade['fee_usd'] for trade in trades)
+
        row = {
            "timeframe": rule_name,
            "stop_loss_pct": stop_loss_pct,
@@ -75,6 +79,7 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
            "total_fees_usd": total_fees_usd,
        }
        results_rows.append(row)
+
        for trade in trades:
            trade_rows.append({
                "timeframe": rule_name,
@@ -87,7 +92,9 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
                "type": trade.get("type"),
                "fee_usd": trade.get("fee_usd"),
            })
+
        logging.info(f"Timeframe: {rule_name}, Stop Loss: {stop_loss_pct}, Trades: {n_trades}")
+
        if debug:
            for trade in trades:
                if trade['type'] == 'STOP':
@@ -95,13 +102,16 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
            for trade in trades:
                if trade['profit_pct'] < -0.09:  # or whatever is close to -0.10
                    print("Large loss trade:", trade)
+
    return results_rows, trade_rows

 def process(timeframe_info, debug=False):
-    """Process a single (timeframe, stop_loss_pct) combination (no monthly split)"""
+    from cycles.utils.storage import Storage  # import inside function for safety
+    storage = Storage(logging=None)  # or pass a logger if you want, but None is safest for multiprocessing
+
    rule, data_1min, stop_loss_pct, initial_usd = timeframe_info

-    if rule == "1T":
+    if rule == "1T" or rule == "1min":
        df = data_1min.copy()
    else:
        df = data_1min.resample(rule).agg({
@@ -112,7 +122,33 @@ def process(timeframe_info, debug=False):
            'volume': 'sum'
        }).dropna()
    df = df.reset_index()
+
    results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
+
+    if all_trade_rows:
+        trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
+        # Prepare header
+        summary_fields = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "final_usd"]
+        summary_row = results_rows[0]
+        header_line = "\t".join(summary_fields) + "\n"
+        value_line = "\t".join(str(summary_row.get(f, "")) for f in summary_fields) + "\n"
+        # File name
+        tf = summary_row["timeframe"]
+        sl = summary_row["stop_loss_pct"]
+        sl_percent = int(round(sl * 100))
+        trades_filename = os.path.join(storage.results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
+        # Write header
+        with open(trades_filename, "w") as f:
+            f.write(header_line)
+            f.write(value_line)
+        # Now write trades (append mode, skip header)
+        with open(trades_filename, "a", newline="") as f:
+            import csv
+            writer = csv.DictWriter(f, fieldnames=trades_fieldnames)
+            writer.writeheader()
+            for trade in all_trade_rows:
+                writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
+
    return results_rows, all_trade_rows

 def aggregate_results(all_rows):
@@ -126,7 +162,6 @@ def aggregate_results(all_rows):

    summary_rows = []
    for (rule, stop_loss_pct), rows in grouped.items():
-        n_months = len(rows)
        total_trades = sum(r['n_trades'] for r in rows)
        total_stop_loss = sum(r['n_stop_loss'] for r in rows)
        avg_win_rate = np.mean([r['win_rate'] for r in rows])
@@ -163,7 +198,7 @@ def get_nearest_price(df, target_date):
        return nearest_time, price

 if __name__ == "__main__":
-    debug = True
+    debug = False

    parser = argparse.ArgumentParser(description="Run backtest with config file.")
    parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
@@ -171,11 +206,11 @@ if __name__ == "__main__":

    # Default values (from config.json)
    default_config = {
-        "start_date": "2024-05-15",
+        "start_date": "2025-05-01",
        "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
        "initial_usd": 10000,
-        "timeframes": ["1D"],
-        "stop_loss_pcts": [0.01, 0.02, 0.03],
+        "timeframes": ["1D", "6h", "3h", "1h", "30m", "15m", "5m", "1m"],
+        "stop_loss_pcts": [0.01, 0.02, 0.03, 0.05],
    }

    if args.config:
@@ -238,6 +273,7 @@ if __name__ == "__main__":
    if debug:
        all_results_rows = []
        all_trade_rows = []
+
        for task in tasks:
            results, trades = process(task, debug)
            if results or trades:
@@ -263,7 +299,4 @@ if __name__ == "__main__":
    ]
    storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)

-    trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
-    storage.write_trades(all_trade_rows, trades_fieldnames)
-
    
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,10 +5,15 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
+    "dash>=3.0.4",
    "gspread>=6.2.1",
    "matplotlib>=3.10.3",
+    "numba>=0.61.2",
    "pandas>=2.2.3",
    "psutil>=7.0.0",
+    "scikit-learn>=1.6.1",
    "scipy>=1.15.3",
    "seaborn>=0.13.2",
+    "ta>=0.11.0",
+    "xgboost>=3.0.2",
 ]
--- a/requirements.txt
+++ b/requirements.txt
--- a/uv.lock
+++ b/uv.lock
@@ -7,6 +7,15 @@ resolution-markers = [
    "python_full_version < '3.11'",
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+[[package]]
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+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
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 version = "5.5.2"
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 ]

+[[package]]
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+version = "8.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
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+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
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+
 [[package]]
 name = "contourpy"
 version = "1.3.2"
@@ -167,22 +197,68 @@ name = "cycles"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "dash" },
    { name = "gspread" },
    { name = "matplotlib" },
+    { name = "numba" },
    { name = "pandas" },
    { name = "psutil" },
+    { name = "scikit-learn" },
    { name = "scipy" },
    { name = "seaborn" },
+    { name = "ta" },
+    { name = "xgboost" },
 ]

 [package.metadata]
 requires-dist = [
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    { name = "gspread", specifier = ">=6.2.1" },
    { name = "matplotlib", specifier = ">=3.10.3" },
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    { name = "pandas", specifier = ">=2.2.3" },
    { name = "psutil", specifier = ">=7.0.0" },
+    { name = "scikit-learn", specifier = ">=1.6.1" },
    { name = "scipy", specifier = ">=1.15.3" },
    { name = "seaborn", specifier = ">=0.13.2" },
+    { name = "ta", specifier = ">=0.11.0" },
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+]
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+version = "3.0.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "importlib-metadata" },
+    { name = "nest-asyncio" },
+    { name = "plotly" },
+    { name = "requests" },
+    { name = "retrying" },
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+]
--- a/xgboost/custom_xgboost.py
+++ b/xgboost/custom_xgboost.py
@@ -0,0 +1,39 @@
+import xgboost as xgb
+import numpy as np
+
+class CustomXGBoostGPU:
+    def __init__(self, X_train, X_test, y_train, y_test):
+        self.X_train = X_train.astype(np.float32)
+        self.X_test = X_test.astype(np.float32)
+        self.y_train = y_train.astype(np.float32)
+        self.y_test = y_test.astype(np.float32)
+        self.model = None
+        self.params = None  # Will be set during training
+
+    def train(self, **xgb_params):
+        params = {
+            'tree_method': 'hist',
+            'device': 'cuda',
+            'objective': 'reg:squarederror',
+            'eval_metric': 'rmse',
+            'verbosity': 1,
+        }
+        params.update(xgb_params)
+        self.params = params  # Store params for later access
+        dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
+        dtest = xgb.DMatrix(self.X_test, label=self.y_test)
+        evals = [(dtrain, 'train'), (dtest, 'eval')]
+        self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
+        return self.model
+
+    def predict(self, X):
+        if self.model is None:
+            raise ValueError('Model not trained yet.')
+        dmatrix = xgb.DMatrix(X.astype(np.float32))
+        return self.model.predict(dmatrix)
+
+    def save_model(self, file_path):
+        """Save the trained XGBoost model to the specified file path."""
+        if self.model is None:
+            raise ValueError('Model not trained yet.')
+        self.model.save_model(file_path)
--- a/xgboost/main.py
+++ b/xgboost/main.py
@@ -0,0 +1,806 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+import pandas as pd
+import numpy as np
+from custom_xgboost import CustomXGBoostGPU
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from plot_results import plot_prediction_error_distribution, plot_direction_transition_heatmap
+from cycles.supertrend import Supertrends
+import time
+from numba import njit
+import itertools
+import csv
+import pandas_ta as ta
+
+def run_indicator(func, *args):
+    return func(*args)
+
+def run_indicator_job(job):
+    import time
+    func, *args = job
+    indicator_name = func.__name__
+    start = time.time()
+    result = func(*args)
+    elapsed = time.time() - start
+    print(f'Indicator {indicator_name} computed in {elapsed:.4f} seconds')
+    return result
+
+def calc_rsi(close):
+    from ta.momentum import RSIIndicator
+    return ('rsi', RSIIndicator(close, window=14).rsi())
+
+def calc_macd(close):
+    from ta.trend import MACD
+    return ('macd', MACD(close).macd())
+
+def calc_bollinger(close):
+    from ta.volatility import BollingerBands
+    bb = BollingerBands(close=close, window=20, window_dev=2)
+    return [
+        ('bb_bbm', bb.bollinger_mavg()),
+        ('bb_bbh', bb.bollinger_hband()),
+        ('bb_bbl', bb.bollinger_lband()),
+        ('bb_bb_width', bb.bollinger_hband() - bb.bollinger_lband())
+    ]
+
+def calc_stochastic(high, low, close):
+    from ta.momentum import StochasticOscillator
+    stoch = StochasticOscillator(high=high, low=low, close=close, window=14, smooth_window=3)
+    return [
+        ('stoch_k', stoch.stoch()),
+        ('stoch_d', stoch.stoch_signal())
+    ]
+
+def calc_atr(high, low, close):
+    from ta.volatility import AverageTrueRange
+    atr = AverageTrueRange(high=high, low=low, close=close, window=14)
+    return ('atr', atr.average_true_range())
+
+def calc_cci(high, low, close):
+    from ta.trend import CCIIndicator
+    cci = CCIIndicator(high=high, low=low, close=close, window=20)
+    return ('cci', cci.cci())
+
+def calc_williamsr(high, low, close):
+    from ta.momentum import WilliamsRIndicator
+    willr = WilliamsRIndicator(high=high, low=low, close=close, lbp=14)
+    return ('williams_r', willr.williams_r())
+
+def calc_ema(close):
+    from ta.trend import EMAIndicator
+    ema = EMAIndicator(close=close, window=14)
+    return ('ema_14', ema.ema_indicator())
+
+def calc_obv(close, volume):
+    from ta.volume import OnBalanceVolumeIndicator
+    obv = OnBalanceVolumeIndicator(close=close, volume=volume)
+    return ('obv', obv.on_balance_volume())
+
+def calc_cmf(high, low, close, volume):
+    from ta.volume import ChaikinMoneyFlowIndicator
+    cmf = ChaikinMoneyFlowIndicator(high=high, low=low, close=close, volume=volume, window=20)
+    return ('cmf', cmf.chaikin_money_flow())
+
+def calc_sma(close):
+    from ta.trend import SMAIndicator
+    return [
+        ('sma_50', SMAIndicator(close, window=50).sma_indicator()),
+        ('sma_200', SMAIndicator(close, window=200).sma_indicator())
+    ]
+
+def calc_roc(close):
+    from ta.momentum import ROCIndicator
+    return ('roc_10', ROCIndicator(close, window=10).roc())
+
+def calc_momentum(close):
+    return ('momentum_10', close - close.shift(10))
+
+def calc_psar(high, low, close):
+    # Use the Numba-accelerated fast_psar function for speed
+    psar_values = fast_psar(np.array(high), np.array(low), np.array(close))
+    return [('psar', pd.Series(psar_values, index=close.index))]
+
+def calc_donchian(high, low, close):
+    from ta.volatility import DonchianChannel
+    donchian = DonchianChannel(high, low, close, window=20)
+    return [
+        ('donchian_hband', donchian.donchian_channel_hband()),
+        ('donchian_lband', donchian.donchian_channel_lband()),
+        ('donchian_mband', donchian.donchian_channel_mband())
+    ]
+
+def calc_keltner(high, low, close):
+    from ta.volatility import KeltnerChannel
+    keltner = KeltnerChannel(high, low, close, window=20)
+    return [
+        ('keltner_hband', keltner.keltner_channel_hband()),
+        ('keltner_lband', keltner.keltner_channel_lband()),
+        ('keltner_mband', keltner.keltner_channel_mband())
+    ]
+
+def calc_dpo(close):
+    from ta.trend import DPOIndicator
+    return ('dpo_20', DPOIndicator(close, window=20).dpo())
+
+def calc_ultimate(high, low, close):
+    from ta.momentum import UltimateOscillator
+    return ('ultimate_osc', UltimateOscillator(high, low, close).ultimate_oscillator())
+
+def calc_ichimoku(high, low):
+    from ta.trend import IchimokuIndicator
+    ichimoku = IchimokuIndicator(high, low, window1=9, window2=26, window3=52)
+    return [
+        ('ichimoku_a', ichimoku.ichimoku_a()),
+        ('ichimoku_b', ichimoku.ichimoku_b()),
+        ('ichimoku_base_line', ichimoku.ichimoku_base_line()),
+        ('ichimoku_conversion_line', ichimoku.ichimoku_conversion_line())
+    ]
+
+def calc_elder_ray(close, low, high):
+    from ta.trend import EMAIndicator
+    ema = EMAIndicator(close, window=13).ema_indicator()
+    return [
+        ('elder_ray_bull', ema - low),
+        ('elder_ray_bear', ema - high)
+    ]
+
+def calc_daily_return(close):
+    from ta.others import DailyReturnIndicator
+    return ('daily_return', DailyReturnIndicator(close).daily_return())
+
+@njit
+def fast_psar(high, low, close, af=0.02, max_af=0.2):
+    length = len(close)
+    psar = np.zeros(length)
+    bull = True
+    af_step = af
+    ep = low[0]
+    psar[0] = low[0]
+    for i in range(1, length):
+        prev_psar = psar[i-1]
+        if bull:
+            psar[i] = prev_psar + af_step * (ep - prev_psar)
+            if low[i] < psar[i]:
+                bull = False
+                psar[i] = ep
+                af_step = af
+                ep = low[i]
+            else:
+                if high[i] > ep:
+                    ep = high[i]
+                    af_step = min(af_step + af, max_af)
+        else:
+            psar[i] = prev_psar + af_step * (ep - prev_psar)
+            if high[i] > psar[i]:
+                bull = True
+                psar[i] = ep
+                af_step = af
+                ep = high[i]
+            else:
+                if low[i] < ep:
+                    ep = low[i]
+                    af_step = min(af_step + af, max_af)
+    return psar
+
+def compute_lag(df, col, lag):
+    return df[col].shift(lag)
+
+def compute_rolling(df, col, stat, window):
+    if stat == 'mean':
+        return df[col].rolling(window).mean()
+    elif stat == 'std':
+        return df[col].rolling(window).std()
+    elif stat == 'min':
+        return df[col].rolling(window).min()
+    elif stat == 'max':
+        return df[col].rolling(window).max()
+
+def compute_log_return(df, horizon):
+    return np.log(df['Close'] / df['Close'].shift(horizon))
+
+def compute_volatility(df, window):
+    return df['log_return'].rolling(window).std()
+
+def run_feature_job(job, df):
+    feature_name, func, *args = job
+    print(f'Computing feature: {feature_name}')
+    result = func(df, *args)
+    return feature_name, result
+
+def calc_adx(high, low, close):
+    from ta.trend import ADXIndicator
+    adx = ADXIndicator(high=high, low=low, close=close, window=14)
+    return [
+        ('adx', adx.adx()),
+        ('adx_pos', adx.adx_pos()),
+        ('adx_neg', adx.adx_neg())
+    ]
+
+def calc_trix(close):
+    from ta.trend import TRIXIndicator
+    trix = TRIXIndicator(close=close, window=15)
+    return ('trix', trix.trix())
+
+def calc_vortex(high, low, close):
+    from ta.trend import VortexIndicator
+    vortex = VortexIndicator(high=high, low=low, close=close, window=14)
+    return [
+        ('vortex_pos', vortex.vortex_indicator_pos()),
+        ('vortex_neg', vortex.vortex_indicator_neg())
+    ]
+
+def calc_kama(close):
+    import pandas_ta as ta
+    kama = ta.kama(close, length=10)
+    return ('kama', kama)
+
+def calc_force_index(close, volume):
+    from ta.volume import ForceIndexIndicator
+    fi = ForceIndexIndicator(close=close, volume=volume, window=13)
+    return ('force_index', fi.force_index())
+
+def calc_eom(high, low, volume):
+    from ta.volume import EaseOfMovementIndicator
+    eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
+    return ('eom', eom.ease_of_movement())
+
+def calc_mfi(high, low, close, volume):
+    from ta.volume import MFIIndicator
+    mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
+    return ('mfi', mfi.money_flow_index())
+
+def calc_adi(high, low, close, volume):
+    from ta.volume import AccDistIndexIndicator
+    adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
+    return ('adi', adi.acc_dist_index())
+
+def calc_tema(close):
+    import pandas_ta as ta
+    tema = ta.tema(close, length=10)
+    return ('tema', tema)
+
+def calc_stochrsi(close):
+    from ta.momentum import StochRSIIndicator
+    stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
+    return [
+        ('stochrsi', stochrsi.stochrsi()),
+        ('stochrsi_k', stochrsi.stochrsi_k()),
+        ('stochrsi_d', stochrsi.stochrsi_d())
+    ]
+
+def calc_awesome_oscillator(high, low):
+    from ta.momentum import AwesomeOscillatorIndicator
+    ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
+    return ('awesome_osc', ao.awesome_oscillator())
+
+if __name__ == '__main__':
+    IMPUTE_NANS = True  # Set to True to impute NaNs, False to drop rows with NaNs
+    csv_path = './data/btcusd_1-min_data.csv'
+    csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
+
+    print('Reading CSV and filtering data...')
+    df = pd.read_csv(csv_path)
+    df = df[df['Volume'] != 0]
+
+    min_date = '2017-06-01'
+    print('Converting Timestamp and filtering by date...')
+    df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
+    df = df[df['Timestamp'] >= min_date]
+
+    lags = 3
+
+    print('Calculating log returns as the new target...')
+    df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
+
+    ohlcv_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
+    window_sizes = [5, 15, 30]  # in minutes, adjust as needed
+
+    features_dict = {}
+
+    print('Starting feature computation...')
+    feature_start_time = time.time()
+
+    # --- Technical Indicator Features: Calculate or Load from Cache ---
+    print('Calculating or loading technical indicator features...')
+    # RSI
+    feature_file = f'./data/{csv_prefix}_rsi.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['rsi'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: rsi')
+        _, values = calc_rsi(df['Close'])
+        features_dict['rsi'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # MACD
+    feature_file = f'./data/{csv_prefix}_macd.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['macd'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: macd')
+        _, values = calc_macd(df['Close'])
+        features_dict['macd'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # ATR
+    feature_file = f'./data/{csv_prefix}_atr.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['atr'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: atr')
+        _, values = calc_atr(df['High'], df['Low'], df['Close'])
+        features_dict['atr'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # CCI
+    feature_file = f'./data/{csv_prefix}_cci.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['cci'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: cci')
+        _, values = calc_cci(df['High'], df['Low'], df['Close'])
+        features_dict['cci'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Williams %R
+    feature_file = f'./data/{csv_prefix}_williams_r.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['williams_r'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: williams_r')
+        _, values = calc_williamsr(df['High'], df['Low'], df['Close'])
+        features_dict['williams_r'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # EMA 14
+    feature_file = f'./data/{csv_prefix}_ema_14.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['ema_14'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: ema_14')
+        _, values = calc_ema(df['Close'])
+        features_dict['ema_14'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # OBV
+    feature_file = f'./data/{csv_prefix}_obv.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['obv'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: obv')
+        _, values = calc_obv(df['Close'], df['Volume'])
+        features_dict['obv'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # CMF
+    feature_file = f'./data/{csv_prefix}_cmf.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['cmf'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: cmf')
+        _, values = calc_cmf(df['High'], df['Low'], df['Close'], df['Volume'])
+        features_dict['cmf'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # ROC 10
+    feature_file = f'./data/{csv_prefix}_roc_10.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['roc_10'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: roc_10')
+        _, values = calc_roc(df['Close'])
+        features_dict['roc_10'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # DPO 20
+    feature_file = f'./data/{csv_prefix}_dpo_20.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['dpo_20'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: dpo_20')
+        _, values = calc_dpo(df['Close'])
+        features_dict['dpo_20'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Ultimate Oscillator
+    feature_file = f'./data/{csv_prefix}_ultimate_osc.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['ultimate_osc'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: ultimate_osc')
+        _, values = calc_ultimate(df['High'], df['Low'], df['Close'])
+        features_dict['ultimate_osc'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Daily Return
+    feature_file = f'./data/{csv_prefix}_daily_return.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['daily_return'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: daily_return')
+        _, values = calc_daily_return(df['Close'])
+        features_dict['daily_return'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Multi-column indicators
+    # Bollinger Bands
+    print('Calculating multi-column indicator: bollinger')
+    result = calc_bollinger(df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Stochastic Oscillator
+    print('Calculating multi-column indicator: stochastic')
+    result = calc_stochastic(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # SMA
+    print('Calculating multi-column indicator: sma')
+    result = calc_sma(df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # PSAR
+    print('Calculating multi-column indicator: psar')
+    result = calc_psar(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Donchian Channel
+    print('Calculating multi-column indicator: donchian')
+    result = calc_donchian(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Keltner Channel
+    print('Calculating multi-column indicator: keltner')
+    result = calc_keltner(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Ichimoku
+    print('Calculating multi-column indicator: ichimoku')
+    result = calc_ichimoku(df['High'], df['Low'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Elder Ray
+    print('Calculating multi-column indicator: elder_ray')
+    result = calc_elder_ray(df['Close'], df['Low'], df['High'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Prepare lags, rolling stats, log returns, and volatility features sequentially
+    # Lags
+    for col in ohlcv_cols:
+        for lag in range(1, lags + 1):
+            feature_name = f'{col}_lag{lag}'
+            feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
+            if os.path.exists(feature_file):
+                print(f'C Loading cached feature: {feature_file}')
+                features_dict[feature_name] = np.load(feature_file)
+            else:
+                print(f'Computing lag feature: {feature_name}')
+                result = compute_lag(df, col, lag)
+                features_dict[feature_name] = result
+                np.save(feature_file, result.values)
+                print(f'Saved feature: {feature_file}')
+    # Rolling statistics
+    for col in ohlcv_cols:
+        for window in window_sizes:
+            if (col == 'Open' and window == 5):
+                continue
+            if (col == 'High' and window == 5):
+                continue
+            if (col == 'High' and window == 30):
+                continue
+            if (col == 'Low' and window == 15):
+                continue
+            for stat in ['mean', 'std', 'min', 'max']:
+                feature_name = f'{col}_roll_{stat}_{window}'
+                feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
+                if os.path.exists(feature_file):
+                    print(f'D Loading cached feature: {feature_file}')
+                    features_dict[feature_name] = np.load(feature_file)
+                else:
+                    print(f'Computing rolling stat feature: {feature_name}')
+                    result = compute_rolling(df, col, stat, window)
+                    features_dict[feature_name] = result
+                    np.save(feature_file, result.values)
+                    print(f'Saved feature: {feature_file}')
+    # Log returns for different horizons
+    for horizon in [5, 15, 30]:
+        feature_name = f'log_return_{horizon}'
+        feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
+        if os.path.exists(feature_file):
+            print(f'E Loading cached feature: {feature_file}')
+            features_dict[feature_name] = np.load(feature_file)
+        else:
+            print(f'Computing log return feature: {feature_name}')
+            result = compute_log_return(df, horizon)
+            features_dict[feature_name] = result
+            np.save(feature_file, result.values)
+            print(f'Saved feature: {feature_file}')
+    # Volatility
+    for window in window_sizes:
+        feature_name = f'volatility_{window}'
+        feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
+        if os.path.exists(feature_file):
+            print(f'F Loading cached feature: {feature_file}')
+            features_dict[feature_name] = np.load(feature_file)
+        else:
+            print(f'Computing volatility feature: {feature_name}')
+            result = compute_volatility(df, window)
+            features_dict[feature_name] = result
+            np.save(feature_file, result.values)
+            print(f'Saved feature: {feature_file}')
+
+    # --- Additional Technical Indicator Features ---
+    # ADX
+    adx_names = ['adx', 'adx_pos', 'adx_neg']
+    adx_files = [f'./data/{csv_prefix}_{name}.npy' for name in adx_names]
+    if all(os.path.exists(f) for f in adx_files):
+        print('G Loading cached features: ADX')
+        for name, f in zip(adx_names, adx_files):
+            arr = np.load(f)
+            features_dict[name] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating multi-column indicator: adx')
+        result = calc_adx(df['High'], df['Low'], df['Close'])
+        for subname, values in result:
+            sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Force Index
+    feature_file = f'./data/{csv_prefix}_force_index.npy'
+    if os.path.exists(feature_file):
+        print(f'K Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['force_index'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: force_index')
+        _, values = calc_force_index(df['Close'], df['Volume'])
+        features_dict['force_index'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Supertrend indicators
+    for period, multiplier in [(12, 3.0), (10, 1.0), (11, 2.0)]:
+        st_name = f'supertrend_{period}_{multiplier}'
+        st_trend_name = f'supertrend_trend_{period}_{multiplier}'
+        st_file = f'./data/{csv_prefix}_{st_name}.npy'
+        st_trend_file = f'./data/{csv_prefix}_{st_trend_name}.npy'
+        if os.path.exists(st_file) and os.path.exists(st_trend_file):
+            print(f'L Loading cached features: {st_file}, {st_trend_file}')
+            features_dict[st_name] = pd.Series(np.load(st_file), index=df.index)
+            features_dict[st_trend_name] = pd.Series(np.load(st_trend_file), index=df.index)
+        else:
+            print(f'Calculating Supertrend indicator: {st_name}')
+            st = ta.supertrend(df['High'], df['Low'], df['Close'], length=period, multiplier=multiplier)
+            features_dict[st_name] = st[f'SUPERT_{period}_{multiplier}']
+            features_dict[st_trend_name] = st[f'SUPERTd_{period}_{multiplier}']
+            np.save(st_file, features_dict[st_name].values)
+            np.save(st_trend_file, features_dict[st_trend_name].values)
+            print(f'Saved features: {st_file}, {st_trend_file}')
+
+    # Concatenate all new features at once
+    print('Concatenating all new features to DataFrame...')
+    features_df = pd.DataFrame(features_dict)
+    print("Columns in features_df:", features_df.columns.tolist())
+    print("All-NaN columns in features_df:", features_df.columns[features_df.isna().all()].tolist())
+    df = pd.concat([df, features_df], axis=1)
+
+    # Print all columns after concatenation
+    print("All columns in df after concat:", df.columns.tolist())
+
+    # Downcast all float columns to save memory
+    print('Downcasting float columns to save memory...')
+    for col in df.columns:
+        try:
+            df[col] = pd.to_numeric(df[col], downcast='float')
+        except Exception:
+            pass
+
+    # Add time features (exclude 'dayofweek')
+    print('Adding hour feature...')
+    df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+    df['hour'] = df['Timestamp'].dt.hour
+
+    # Handle NaNs after all feature engineering
+    if IMPUTE_NANS:
+        print('Imputing NaNs after feature engineering (using mean imputation)...')
+        numeric_cols = df.select_dtypes(include=[np.number]).columns
+        for col in numeric_cols:
+            df[col] = df[col].fillna(df[col].mean())
+        # If you want to impute non-numeric columns differently, add logic here
+    else:
+        print('Dropping NaNs after feature engineering...')
+        df = df.dropna().reset_index(drop=True)
+
+    # Exclude 'Timestamp', 'Close', 'log_return', and any future target columns from features
+    print('Selecting feature columns...')
+    exclude_cols = ['Timestamp', 'Close', 'log_return', 'log_return_5', 'log_return_15', 'log_return_30']
+    feature_cols = [col for col in df.columns if col not in exclude_cols]
+    print('Features used for training:', feature_cols)
+
+    # Prepare CSV for results
+    results_csv = './data/leave_one_out_results.csv'
+    if not os.path.exists(results_csv):
+        with open(results_csv, 'w', newline='') as f:
+            writer = csv.writer(f)
+            writer.writerow(['left_out_feature', 'used_features', 'rmse', 'mae', 'r2', 'mape', 'directional_accuracy'])
+
+    total_features = len(feature_cols)
+    never_leave_out = {'Open', 'High', 'Low', 'Close', 'Volume'}
+    for idx, left_out in enumerate(feature_cols):
+        if left_out in never_leave_out:
+            continue
+        used = [f for f in feature_cols if f != left_out]
+        print(f'\n=== Leave-one-out {idx+1}/{total_features}: left out {left_out} ===')
+        try:
+            # Prepare X and y for this combination
+            X = df[used].values.astype(np.float32)
+            y = df["log_return"].values.astype(np.float32)
+            split_idx = int(len(X) * 0.8)
+            X_train, X_test = X[:split_idx], X[split_idx:]
+            y_train, y_test = y[:split_idx], y[split_idx:]
+            test_timestamps = df['Timestamp'].values[split_idx:]
+
+            model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
+            booster = model.train()
+            model.save_model(f'./data/xgboost_model_wo_{left_out}.json')
+
+            test_preds = model.predict(X_test)
+            rmse = np.sqrt(mean_squared_error(y_test, test_preds))
+
+            # Reconstruct price series from log returns
+            if 'Close' in df.columns:
+                close_prices = df['Close'].values
+            else:
+                close_prices = pd.read_csv(csv_path)['Close'].values
+            start_price = close_prices[split_idx]
+            actual_prices = [start_price]
+            for r_ in y_test:
+                actual_prices.append(actual_prices[-1] * np.exp(r_))
+            actual_prices = np.array(actual_prices[1:])
+            predicted_prices = [start_price]
+            for r_ in test_preds:
+                predicted_prices.append(predicted_prices[-1] * np.exp(r_))
+            predicted_prices = np.array(predicted_prices[1:])
+
+            mae = mean_absolute_error(actual_prices, predicted_prices)
+            r2 = r2_score(actual_prices, predicted_prices)
+            direction_actual = np.sign(np.diff(actual_prices))
+            direction_pred = np.sign(np.diff(predicted_prices))
+            directional_accuracy = (direction_actual == direction_pred).mean()
+            mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
+
+            # Save results to CSV
+            with open(results_csv, 'a', newline='') as f:
+                writer = csv.writer(f)
+                writer.writerow([left_out, "|".join(used), rmse, mae, r2, mape, directional_accuracy])
+            print(f'Left out {left_out}: RMSE={rmse:.4f}, MAE={mae:.4f}, R2={r2:.4f}, MAPE={mape:.2f}%, DirAcc={directional_accuracy*100:.2f}%')
+
+            # Plotting for this run
+            plot_prefix = f'loo_{left_out}'
+            print('Plotting distribution of absolute prediction errors...')
+            plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
+
+            print('Plotting directional accuracy...')
+            plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=plot_prefix)
+        except Exception as e:
+            print(f'Leave-one-out failed for {left_out}: {e}')
+    print(f'All leave-one-out runs completed. Results saved to {results_csv}')
+    sys.exit(0)
--- a/xgboost/plot_results.py
+++ b/xgboost/plot_results.py
@@ -0,0 +1,318 @@
+import numpy as np
+import dash
+from dash import dcc, html
+import plotly.graph_objs as go
+import threading
+
+
+def display_actual_vs_predicted(y_test, test_preds, timestamps, n_plot=200):
+    import plotly.offline as pyo
+    n_plot = min(n_plot, len(y_test))
+    plot_indices = timestamps[:n_plot]
+    actual = y_test[:n_plot]
+    predicted = test_preds[:n_plot]
+
+    trace_actual = go.Scatter(x=plot_indices, y=actual, mode='lines', name='Actual')
+    trace_predicted = go.Scatter(x=plot_indices, y=predicted, mode='lines', name='Predicted')
+    data = [trace_actual, trace_predicted]
+    layout = go.Layout(
+        title='Actual vs. Predicted BTC Close Prices (Test Set)',
+        xaxis={'title': 'Timestamp'},
+        yaxis={'title': 'BTC Close Price'},
+        legend={'x': 0, 'y': 1},
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig = go.Figure(data=data, layout=layout)
+    pyo.plot(fig, auto_open=False)
+
+def plot_target_distribution(y_train, y_test):
+    import plotly.offline as pyo
+    trace_train = go.Histogram(
+        x=y_train,
+        nbinsx=100,
+        opacity=0.5,
+        name='Train',
+        marker=dict(color='blue')
+    )
+    trace_test = go.Histogram(
+        x=y_test,
+        nbinsx=100,
+        opacity=0.5,
+        name='Test',
+        marker=dict(color='orange')
+    )
+    data = [trace_train, trace_test]
+    layout = go.Layout(
+        title='Distribution of Target Variable (Close Price)',
+        xaxis=dict(title='BTC Close Price'),
+        yaxis=dict(title='Frequency'),
+        barmode='overlay'
+    )
+    fig = go.Figure(data=data, layout=layout)
+    pyo.plot(fig, auto_open=False)
+
+def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_plot=200):
+    import plotly.offline as pyo
+    import plotly.graph_objs as go
+    n_plot = min(n_plot, len(y_test))
+    actual = y_test[:n_plot]
+    predicted = test_preds[:n_plot]
+    if timestamps is not None:
+        x_axis = timestamps[:n_plot]
+        x_label = 'Timestamp'
+    else:
+        x_axis = list(range(n_plot))
+        x_label = 'Index'
+
+    # Line plot: Actual vs Predicted over time
+    trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual')
+    trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted')
+    data_line = [trace_actual, trace_predicted]
+    layout_line = go.Layout(
+        title='Actual vs. Predicted Log Returns (Test Set)',
+        xaxis={'title': x_label},
+        yaxis={'title': 'Log Return'},
+        legend={'x': 0, 'y': 1},
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_line = go.Figure(data=data_line, layout=layout_line)
+    pyo.plot(fig_line, filename='charts/log_return_line_plot.html', auto_open=False)
+
+    # Scatter plot: Predicted vs Actual
+    trace_scatter = go.Scatter(
+        x=actual,
+        y=predicted,
+        mode='markers',
+        name='Predicted vs Actual',
+        opacity=0.5
+    )
+    # Diagonal reference line
+    min_val = min(np.min(actual), np.min(predicted))
+    max_val = max(np.max(actual), np.max(predicted))
+    trace_diag = go.Scatter(
+        x=[min_val, max_val],
+        y=[min_val, max_val],
+        mode='lines',
+        name='Ideal',
+        line=dict(dash='dash', color='red')
+    )
+    data_scatter = [trace_scatter, trace_diag]
+    layout_scatter = go.Layout(
+        title='Predicted vs Actual Log Returns (Scatter)',
+        xaxis={'title': 'Actual Log Return'},
+        yaxis={'title': 'Predicted Log Return'},
+        showlegend=True,
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
+    pyo.plot(fig_scatter, filename='charts/log_return_scatter_plot.html', auto_open=False)
+
+def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200):
+    import plotly.offline as pyo
+    import plotly.graph_objs as go
+    n_plot = min(n_plot, len(actual_prices))
+    actual = actual_prices[:n_plot]
+    predicted = predicted_prices[:n_plot]
+    if timestamps is not None:
+        x_axis = timestamps[:n_plot]
+        x_label = 'Timestamp'
+    else:
+        x_axis = list(range(n_plot))
+        x_label = 'Index'
+
+    # Line plot: Actual vs Predicted over time
+    trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual Price')
+    trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted Price')
+    data_line = [trace_actual, trace_predicted]
+    layout_line = go.Layout(
+        title='Actual vs. Predicted BTC Prices (Test Set)',
+        xaxis={'title': x_label},
+        yaxis={'title': 'BTC Price'},
+        legend={'x': 0, 'y': 1},
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_line = go.Figure(data=data_line, layout=layout_line)
+    pyo.plot(fig_line, filename='charts/price_line_plot.html', auto_open=False)
+
+    # Scatter plot: Predicted vs Actual
+    trace_scatter = go.Scatter(
+        x=actual,
+        y=predicted,
+        mode='markers',
+        name='Predicted vs Actual',
+        opacity=0.5
+    )
+    # Diagonal reference line
+    min_val = min(np.min(actual), np.min(predicted))
+    max_val = max(np.max(actual), np.max(predicted))
+    trace_diag = go.Scatter(
+        x=[min_val, max_val],
+        y=[min_val, max_val],
+        mode='lines',
+        name='Ideal',
+        line=dict(dash='dash', color='red')
+    )
+    data_scatter = [trace_scatter, trace_diag]
+    layout_scatter = go.Layout(
+        title='Predicted vs Actual Prices (Scatter)',
+        xaxis={'title': 'Actual Price'},
+        yaxis={'title': 'Predicted Price'},
+        showlegend=True,
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
+    pyo.plot(fig_scatter, filename='charts/price_scatter_plot.html', auto_open=False)
+
+def plot_prediction_error_distribution(predicted_prices, actual_prices, nbins=100, prefix=""):
+    """
+    Plots the distribution of signed prediction errors between predicted and actual prices,
+    coloring negative errors (under-prediction) and positive errors (over-prediction) differently.
+    """
+    import plotly.offline as pyo
+    import plotly.graph_objs as go
+    errors = np.array(predicted_prices) - np.array(actual_prices)
+    
+    # Separate negative and positive errors
+    neg_errors = errors[errors < 0]
+    pos_errors = errors[errors >= 0]
+
+    # Calculate common bin edges
+    min_error = np.min(errors)
+    max_error = np.max(errors)
+    bin_edges = np.linspace(min_error, max_error, nbins + 1)
+    xbins = dict(start=min_error, end=max_error, size=(max_error - min_error) / nbins)
+
+    trace_neg = go.Histogram(
+        x=neg_errors,
+        opacity=0.75,
+        marker=dict(color='blue'),
+        name='Negative Error (Under-prediction)',
+        xbins=xbins
+    )
+    trace_pos = go.Histogram(
+        x=pos_errors,
+        opacity=0.75,
+        marker=dict(color='orange'),
+        name='Positive Error (Over-prediction)',
+        xbins=xbins
+    )
+    layout = go.Layout(
+        title='Distribution of Prediction Errors (Signed)',
+        xaxis=dict(title='Prediction Error (Predicted - Actual)'),
+        yaxis=dict(title='Frequency'),
+        barmode='overlay',
+        bargap=0.05
+    )
+    fig = go.Figure(data=[trace_neg, trace_pos], layout=layout)
+    filename = f'charts/{prefix}_prediction_error_distribution.html'
+    pyo.plot(fig, filename=filename, auto_open=False)
+
+def plot_directional_accuracy(actual_prices, predicted_prices, timestamps=None, n_plot=200):
+    """
+    Plots the directional accuracy of predictions compared to actual price movements.
+    Shows whether the predicted direction matches the actual direction of price movement.
+    
+    Args:
+        actual_prices: Array of actual price values
+        predicted_prices: Array of predicted price values  
+        timestamps: Optional array of timestamps for x-axis
+        n_plot: Number of points to plot (default 200, plots last n_plot points)
+    """
+    import plotly.graph_objs as go
+    import plotly.offline as pyo
+    import numpy as np
+    
+    # Calculate price changes
+    actual_changes = np.diff(actual_prices)
+    predicted_changes = np.diff(predicted_prices)
+    
+    # Determine if directions match
+    actual_direction = np.sign(actual_changes)
+    predicted_direction = np.sign(predicted_changes)
+    correct_direction = actual_direction == predicted_direction
+    
+    # Get last n_plot points
+    actual_changes = actual_changes[-n_plot:]
+    predicted_changes = predicted_changes[-n_plot:]
+    correct_direction = correct_direction[-n_plot:]
+    
+    if timestamps is not None:
+        x_values = timestamps[1:] # Skip first since we took diff
+        x_values = x_values[-n_plot:] # Get last n_plot points
+    else:
+        x_values = list(range(len(actual_changes)))
+    
+    # Create traces for correct and incorrect predictions
+    correct_trace = go.Scatter(
+        x=np.array(x_values)[correct_direction],
+        y=actual_changes[correct_direction],
+        mode='markers',
+        name='Correct Direction',
+        marker=dict(color='green', size=8)
+    )
+    
+    incorrect_trace = go.Scatter(
+        x=np.array(x_values)[~correct_direction],
+        y=actual_changes[~correct_direction],
+        mode='markers',
+        name='Incorrect Direction',
+        marker=dict(color='red', size=8)
+    )
+    
+    # Calculate accuracy percentage
+    accuracy = np.mean(correct_direction) * 100
+    
+    layout = go.Layout(
+        title=f'Directional Accuracy (Overall: {accuracy:.1f}%)',
+        xaxis=dict(title='Time' if timestamps is not None else 'Sample'),
+        yaxis=dict(title='Price Change'),
+        showlegend=True
+    )
+    
+    fig = go.Figure(data=[correct_trace, incorrect_trace], layout=layout)
+    pyo.plot(fig, filename='charts/directional_accuracy.html', auto_open=False)
+
+def plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=""):
+    """
+    Plots a heatmap showing the frequency of each (actual, predicted) direction pair.
+    """
+    import numpy as np
+    import plotly.graph_objs as go
+    import plotly.offline as pyo
+
+    # Calculate directions
+    actual_direction = np.sign(np.diff(actual_prices))
+    predicted_direction = np.sign(np.diff(predicted_prices))
+
+    # Build 3x3 matrix: rows=actual, cols=predicted, values=counts
+    # Map -1 -> 0, 0 -> 1, 1 -> 2 for indexing
+    mapping = {-1: 0, 0: 1, 1: 2}
+    matrix = np.zeros((3, 3), dtype=int)
+    for a, p in zip(actual_direction, predicted_direction):
+        matrix[mapping[a], mapping[p]] += 1
+
+    # Axis labels
+    directions = ['Down (-1)', 'No Change (0)', 'Up (+1)']
+
+    # Plot heatmap
+    heatmap = go.Heatmap(
+        z=matrix,
+        x=directions,  # predicted
+        y=directions,  # actual
+        colorscale='Viridis',
+        colorbar=dict(title='Count')
+    )
+    layout = go.Layout(
+        title='Direction Prediction Transition Matrix',
+        xaxis=dict(title='Predicted Direction'),
+        yaxis=dict(title='Actual Direction')
+    )
+    fig = go.Figure(data=[heatmap], layout=layout)
+    filename = f'charts/{prefix}_direction_transition_heatmap.html'
+    pyo.plot(fig, filename=filename, auto_open=False)
+
Author	SHA1	Message	Date
Simon Moisy	a877f14e65	loo on features	2025-05-30 20:06:28 +08:00
Simon Moisy	082a2835b6	Implemented Supertrend indicators for feature engineering in main.py, including caching of computed features. Updated plotting functions in plot_results.py to save charts in a dedicated directory and added new functions for directional accuracy and prediction transition heatmaps.	2025-05-30 18:14:42 +08:00
Simon Moisy	ada6150413	Added multiple technical indicators for feature engineering, including ADX, TRIX, Vortex, KAMA, Force Index, EOM, MFI, ADI, TEMA, StochRSI, and Awesome Oscillator. Improved NaN handling and implemented leave-one-out feature evaluation with results saved to CSV.	2025-05-30 17:59:09 +08:00
Simon Moisy	ced64825bd	reverted to sequential computing for features, added one distribution visualization graph	2025-05-30 15:54:48 +08:00
Simon Moisy	2f98463df8	more uv updates	2025-05-30 15:54:14 +08:00
Simon Moisy	2a52ffde9a	cleanup and uv updates	2025-05-30 15:36:43 +08:00
Simon Moisy	a22914731f	gitignore updated, model file	2025-05-30 12:31:20 +08:00
Simon Moisy	81e4b640a7	model updated	2025-05-30 12:29:37 +08:00
Simon Moisy	2dba88b620	Added mode indicators, still WIP	2025-05-29 12:45:45 +08:00
Simon Moisy	de67b27e37	XGBoost first iteration	2025-05-29 18:28:53 +08:00
Simon Moisy	1284549106	progress print	2025-05-29 11:04:03 +08:00
Simon Moisy	5f03524d6a	never fallback to default values for fee_usd	2025-05-28 02:50:40 +08:00
Simon Moisy	74c8048ed5	shifted one day back on the metatrend to avoid lookahead bias, reverted metatrend calculus to use no cpu optimization for readability	2025-05-27 17:49:55 +08:00