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8 Commits
Incrementa
...
2a52ffde9a
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4
.gitignore
vendored
4
.gitignore
vendored
@@ -1,11 +1,13 @@
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# ---> Python
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*.json
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/data/*.db
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/credentials/*.json
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*.csv
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*.png
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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/data/*.npy
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# C extensions
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*.so
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@@ -1 +0,0 @@
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3.10
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@@ -1,5 +1,6 @@
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import pandas as pd
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import numpy as np
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import time
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from cycles.supertrend import Supertrends
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from cycles.market_fees import MarketFees
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@@ -27,6 +28,9 @@ class Backtest:
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trends_arr = np.stack(trends, axis=1)
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meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]),
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trends_arr[:,0], 0)
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# Shift meta_trend by one to avoid lookahead bias
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meta_trend_signal = np.roll(meta_trend, 1)
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meta_trend_signal[0] = 0 # or np.nan, but 0 means 'no signal' for first bar
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position = 0 # 0 = no position, 1 = long
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entry_price = 0
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@@ -39,14 +43,22 @@ class Backtest:
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entry_time = None
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current_trade_min1_start_idx = None
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min1_df['timestamp'] = pd.to_datetime(min1_df.index)
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min1_df.index = pd.to_datetime(min1_df.index)
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min1_timestamps = min1_df.index.values
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last_print_time = time.time()
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for i in range(1, len(_df)):
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current_time = time.time()
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if current_time - last_print_time >= 5:
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progress = (i / len(_df)) * 100
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print(f"\rProgress: {progress:.1f}%", end="", flush=True)
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last_print_time = current_time
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price_open = _df['open'].iloc[i]
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price_close = _df['close'].iloc[i]
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date = _df['timestamp'].iloc[i]
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prev_mt = meta_trend[i-1]
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curr_mt = meta_trend[i]
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prev_mt = meta_trend_signal[i-1]
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curr_mt = meta_trend_signal[i]
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# Check stop loss if in position
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if position == 1:
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@@ -87,6 +99,8 @@ class Backtest:
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drawdown = (max_balance - balance) / max_balance
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drawdowns.append(drawdown)
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print("\rProgress: 100%\r\n", end="", flush=True)
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# If still in position at end, sell at last close
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if position == 1:
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exit_result = Backtest.handle_exit(coin, _df['close'].iloc[-1], entry_price, entry_time, _df['timestamp'].iloc[-1])
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@@ -1,70 +1,30 @@
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import pandas as pd
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import numpy as np
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import logging
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from scipy.signal import find_peaks
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from matplotlib.patches import Rectangle
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from scipy import stats
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import concurrent.futures
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from functools import partial
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from functools import lru_cache
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import matplotlib.pyplot as plt
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# Color configuration
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# Plot colors
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DARK_BG_COLOR = '#181C27'
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LEGEND_BG_COLOR = '#333333'
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TITLE_COLOR = 'white'
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AXIS_LABEL_COLOR = 'white'
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# Candlestick colors
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CANDLE_UP_COLOR = '#089981' # Green
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CANDLE_DOWN_COLOR = '#F23645' # Red
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# Marker colors
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MIN_COLOR = 'red'
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MAX_COLOR = 'green'
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# Line style colors
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MIN_LINE_STYLE = 'g--' # Green dashed
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MAX_LINE_STYLE = 'r--' # Red dashed
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SMA7_LINE_STYLE = 'y-' # Yellow solid
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SMA15_LINE_STYLE = 'm-' # Magenta solid
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# SuperTrend colors
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ST_COLOR_UP = 'g-'
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ST_COLOR_DOWN = 'r-'
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# Cache the calculation results by function parameters
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@lru_cache(maxsize=32)
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def cached_supertrend_calculation(period, multiplier, data_tuple):
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# Convert tuple back to numpy arrays
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high = np.array(data_tuple[0])
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low = np.array(data_tuple[1])
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close = np.array(data_tuple[2])
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# Calculate TR and ATR using vectorized operations
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tr = np.zeros_like(close)
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tr[0] = high[0] - low[0]
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hc_range = np.abs(high[1:] - close[:-1])
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lc_range = np.abs(low[1:] - close[:-1])
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hl_range = high[1:] - low[1:]
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tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
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# Use numpy's exponential moving average
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atr = np.zeros_like(tr)
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atr[0] = tr[0]
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multiplier_ema = 2.0 / (period + 1)
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for i in range(1, len(tr)):
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atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
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# Calculate bands
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upper_band = np.zeros_like(close)
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lower_band = np.zeros_like(close)
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for i in range(len(close)):
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hl_avg = (high[i] + low[i]) / 2
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upper_band[i] = hl_avg + (multiplier * atr[i])
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lower_band[i] = hl_avg - (multiplier * atr[i])
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final_upper = np.zeros_like(close)
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final_lower = np.zeros_like(close)
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supertrend = np.zeros_like(close)
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@@ -106,76 +66,18 @@ def cached_supertrend_calculation(period, multiplier, data_tuple):
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}
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def calculate_supertrend_external(data, period, multiplier):
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# Convert DataFrame columns to hashable tuples
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high_tuple = tuple(data['high'])
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low_tuple = tuple(data['low'])
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close_tuple = tuple(data['close'])
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# Call the cached function
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return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
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class Supertrends:
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def __init__(self, data, verbose=False, display=False):
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"""
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Initialize the TrendDetectorSimple class.
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Parameters:
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- data: pandas DataFrame containing price data
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- verbose: boolean, whether to display detailed logging information
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- display: boolean, whether to enable display/plotting features
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"""
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self.data = data
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self.verbose = verbose
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self.display = display
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# Only define display-related variables if display is True
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if self.display:
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# Plot style configuration
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self.plot_style = 'dark_background'
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self.bg_color = DARK_BG_COLOR
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self.plot_size = (12, 8)
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# Candlestick configuration
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self.candle_width = 0.6
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self.candle_up_color = CANDLE_UP_COLOR
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self.candle_down_color = CANDLE_DOWN_COLOR
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self.candle_alpha = 0.8
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self.wick_width = 1
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# Marker configuration
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self.min_marker = '^'
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self.min_color = MIN_COLOR
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self.min_size = 100
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self.max_marker = 'v'
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self.max_color = MAX_COLOR
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self.max_size = 100
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self.marker_zorder = 100
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# Line configuration
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self.line_width = 1
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self.min_line_style = MIN_LINE_STYLE
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self.max_line_style = MAX_LINE_STYLE
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self.sma7_line_style = SMA7_LINE_STYLE
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self.sma15_line_style = SMA15_LINE_STYLE
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# Text configuration
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self.title_size = 14
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self.title_color = TITLE_COLOR
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self.axis_label_size = 12
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self.axis_label_color = AXIS_LABEL_COLOR
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# Legend configuration
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self.legend_loc = 'best'
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self.legend_bg_color = LEGEND_BG_COLOR
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# Configure logging
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logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
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format='%(asctime)s - %(levelname)s - %(message)s')
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self.logger = logging.getLogger('TrendDetectorSimple')
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# Convert data to pandas DataFrame if it's not already
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if not isinstance(self.data, pd.DataFrame):
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if isinstance(self.data, list):
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self.data = pd.DataFrame({'close': self.data})
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@@ -183,154 +85,101 @@ class Supertrends:
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raise ValueError("Data must be a pandas DataFrame or a list")
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def calculate_tr(self):
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df = self.data.copy()
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high = df['high'].values
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low = df['low'].values
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close = df['close'].values
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tr = np.zeros_like(close)
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tr[0] = high[0] - low[0]
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for i in range(1, len(close)):
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hl_range = high[i] - low[i]
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hc_range = abs(high[i] - close[i-1])
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lc_range = abs(low[i] - close[i-1])
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tr[i] = max(hl_range, hc_range, lc_range)
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return tr
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def calculate_atr(self, period=14):
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tr = self.calculate_tr()
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atr = np.zeros_like(tr)
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atr[0] = tr[0]
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multiplier = 2.0 / (period + 1)
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for i in range(1, len(tr)):
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atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
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return atr
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def calculate_supertrend(self, period=10, multiplier=3.0):
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"""
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Calculate True Range (TR) for the price data.
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True Range is the greatest of:
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1. Current high - current low
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2. |Current high - previous close|
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3. |Current low - previous close|
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Calculate SuperTrend indicator for the price data.
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SuperTrend is a trend-following indicator that uses ATR to determine the trend direction.
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Parameters:
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- period: int, the period for the ATR calculation (default: 10)
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- multiplier: float, the multiplier for the ATR (default: 3.0)
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Returns:
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- Numpy array of TR values
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- Dictionary containing SuperTrend values, trend direction, and upper/lower bands
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"""
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df = self.data.copy()
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high = df['high'].values
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low = df['low'].values
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close = df['close'].values
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tr = np.zeros_like(close)
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tr[0] = high[0] - low[0] # First TR is just the first day's range
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atr = self.calculate_atr(period)
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upper_band = np.zeros_like(close)
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lower_band = np.zeros_like(close)
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for i in range(len(close)):
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hl_avg = (high[i] + low[i]) / 2
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upper_band[i] = hl_avg + (multiplier * atr[i])
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lower_band[i] = hl_avg - (multiplier * atr[i])
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final_upper = np.zeros_like(close)
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final_lower = np.zeros_like(close)
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supertrend = np.zeros_like(close)
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trend = np.zeros_like(close)
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final_upper[0] = upper_band[0]
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final_lower[0] = lower_band[0]
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if close[0] <= upper_band[0]:
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supertrend[0] = upper_band[0]
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trend[0] = -1
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else:
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supertrend[0] = lower_band[0]
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trend[0] = 1
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for i in range(1, len(close)):
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# Current high - current low
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hl_range = high[i] - low[i]
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# |Current high - previous close|
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hc_range = abs(high[i] - close[i-1])
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# |Current low - previous close|
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lc_range = abs(low[i] - close[i-1])
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# TR is the maximum of these three values
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tr[i] = max(hl_range, hc_range, lc_range)
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return tr
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def calculate_atr(self, period=14):
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"""
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Calculate Average True Range (ATR) for the price data.
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ATR is the exponential moving average of the True Range over a specified period.
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Parameters:
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- period: int, the period for the ATR calculation (default: 14)
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Returns:
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- Numpy array of ATR values
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"""
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tr = self.calculate_tr()
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atr = np.zeros_like(tr)
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# First ATR value is just the first TR
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atr[0] = tr[0]
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# Calculate exponential moving average (EMA) of TR
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multiplier = 2.0 / (period + 1)
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for i in range(1, len(tr)):
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atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
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return atr
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def detect_trends(self):
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"""
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Detect trends by identifying local minima and maxima in the price data
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using scipy.signal.find_peaks.
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Parameters:
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- prominence: float, required prominence of peaks (relative to the price range)
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- width: int, required width of peaks in data points
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Returns:
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- DataFrame with columns for timestamps, prices, and trend indicators
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- Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
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"""
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df = self.data
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# close_prices = df['close'].values
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# max_peaks, _ = find_peaks(close_prices)
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# min_peaks, _ = find_peaks(-close_prices)
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# df['is_min'] = False
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# df['is_max'] = False
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# for peak in max_peaks:
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# df.at[peak, 'is_max'] = True
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# for peak in min_peaks:
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# df.at[peak, 'is_min'] = True
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# result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
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# Perform linear regression on min_peaks and max_peaks
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# min_prices = df['close'].iloc[min_peaks].values
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# max_prices = df['close'].iloc[max_peaks].values
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# Linear regression for min peaks if we have at least 2 points
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# min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
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# Linear regression for max peaks if we have at least 2 points
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# max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
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if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
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final_upper[i] = upper_band[i]
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else:
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final_upper[i] = final_upper[i-1]
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if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
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final_lower[i] = lower_band[i]
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else:
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final_lower[i] = final_lower[i-1]
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if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
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supertrend[i] = final_upper[i]
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trend[i] = -1
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elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
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supertrend[i] = final_lower[i]
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trend[i] = 1
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elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
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supertrend[i] = final_lower[i]
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trend[i] = 1
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elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
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supertrend[i] = final_upper[i]
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trend[i] = -1
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supertrend_results = {
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'supertrend': supertrend,
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'trend': trend,
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'upper_band': final_upper,
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'lower_band': final_lower
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}
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return supertrend_results
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# Calculate Simple Moving Averages (SMA) for 7 and 15 periods
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# sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
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# sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
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analysis_results = {}
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# analysis_results['linear_regression'] = {
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# 'min': {
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# 'slope': min_slope,
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# 'intercept': min_intercept,
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# 'r_squared': min_r_value ** 2
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# },
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# 'max': {
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# 'slope': max_slope,
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# 'intercept': max_intercept,
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# 'r_squared': max_r_value ** 2
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# }
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# }
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# analysis_results['sma'] = {
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# '7': sma_7,
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# '15': sma_15
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# }
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# Calculate SuperTrend indicators
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supertrend_results_list = self._calculate_supertrend_indicators()
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analysis_results['supertrend'] = supertrend_results_list
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return analysis_results
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def calculate_supertrend_indicators(self):
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"""
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Calculate SuperTrend indicators with different parameter sets in parallel.
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Returns:
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- list, the SuperTrend results
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"""
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supertrend_params = [
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{"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
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{"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
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{"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
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{"period": 12, "multiplier": 3.0},
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{"period": 10, "multiplier": 1.0},
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{"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
|
||||
|
||||
1
data/xgboost_model.json
Normal file
1
data/xgboost_model.json
Normal file
File diff suppressed because one or more lines are too long
57
main.py
57
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)
|
||||
|
||||
|
||||
@@ -5,10 +5,12 @@ 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",
|
||||
"pandas>=2.2.3",
|
||||
"psutil>=7.0.0",
|
||||
"scipy>=1.15.3",
|
||||
"seaborn>=0.13.2",
|
||||
"xgboost>=3.0.2",
|
||||
]
|
||||
|
||||
BIN
requirements.txt
BIN
requirements.txt
Binary file not shown.
272
uv.lock
generated
272
uv.lock
generated
@@ -7,6 +7,15 @@ resolution-markers = [
|
||||
"python_full_version < '3.11'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "blinker"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cachetools"
|
||||
version = "5.5.2"
|
||||
@@ -86,6 +95,27 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/20/94/c5790835a017658cbfabd07f3bfb549140c3ac458cfc196323996b10095a/charset_normalizer-3.4.2-py3-none-any.whl", hash = "sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0", size = 52626, upload-time = "2025-05-02T08:34:40.053Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.2.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/60/6c/8ca2efa64cf75a977a0d7fac081354553ebe483345c734fb6b6515d96bbc/click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202", size = 286342, upload-time = "2025-05-20T23:19:49.832Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/85/32/10bb5764d90a8eee674e9dc6f4db6a0ab47c8c4d0d83c27f7c39ac415a4d/click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b", size = 102215, upload-time = "2025-05-20T23:19:47.796Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
version = "0.4.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "contourpy"
|
||||
version = "1.3.2"
|
||||
@@ -167,22 +197,62 @@ name = "cycles"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "dash" },
|
||||
{ name = "gspread" },
|
||||
{ name = "matplotlib" },
|
||||
{ name = "pandas" },
|
||||
{ name = "psutil" },
|
||||
{ name = "scipy" },
|
||||
{ name = "seaborn" },
|
||||
{ name = "xgboost" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "dash", specifier = ">=3.0.4" },
|
||||
{ name = "gspread", specifier = ">=6.2.1" },
|
||||
{ name = "matplotlib", specifier = ">=3.10.3" },
|
||||
{ name = "pandas", specifier = ">=2.2.3" },
|
||||
{ name = "psutil", specifier = ">=7.0.0" },
|
||||
{ name = "scipy", specifier = ">=1.15.3" },
|
||||
{ name = "seaborn", specifier = ">=0.13.2" },
|
||||
{ name = "xgboost", specifier = ">=3.0.2" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dash"
|
||||
version = "3.0.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "flask" },
|
||||
{ name = "importlib-metadata" },
|
||||
{ name = "nest-asyncio" },
|
||||
{ name = "plotly" },
|
||||
{ name = "requests" },
|
||||
{ name = "retrying" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "werkzeug" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/88/6d/90f113317d41266e20190185cf1b5121efbab79ff79b2ecdf8316a91be40/dash-3.0.4.tar.gz", hash = "sha256:4f9e62e9d8c5cd1b42dc6d6dcf211fe9498195f73ef0edb62a26e2a1b952a368", size = 7592060, upload-time = "2025-04-24T19:06:49.287Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0d/20/2e7ab37ea2ef1f8b2592a2615c8b3fb041ad51f32101061d8bc6465b8b40/dash-3.0.4-py3-none-any.whl", hash = "sha256:177f8c3d1fa45555b18f2f670808eba7803c72a6b1cd6fd172fd538aca18eb1d", size = 7935680, upload-time = "2025-04-24T19:06:41.751Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "flask"
|
||||
version = "3.0.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "blinker" },
|
||||
{ name = "click" },
|
||||
{ name = "itsdangerous" },
|
||||
{ name = "jinja2" },
|
||||
{ name = "werkzeug" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/41/e1/d104c83026f8d35dfd2c261df7d64738341067526406b40190bc063e829a/flask-3.0.3.tar.gz", hash = "sha256:ceb27b0af3823ea2737928a4d99d125a06175b8512c445cbd9a9ce200ef76842", size = 676315, upload-time = "2024-04-07T19:26:11.035Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/61/80/ffe1da13ad9300f87c93af113edd0638c75138c42a0994becfacac078c06/flask-3.0.3-py3-none-any.whl", hash = "sha256:34e815dfaa43340d1d15a5c3a02b8476004037eb4840b34910c6e21679d288f3", size = 101735, upload-time = "2024-04-07T19:26:08.569Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -275,6 +345,39 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442, upload-time = "2024-09-15T18:07:37.964Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "8.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "zipp" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641, upload-time = "2025-04-27T15:29:01.736Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/20/b0/36bd937216ec521246249be3bf9855081de4c5e06a0c9b4219dbeda50373/importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd", size = 27656, upload-time = "2025-04-27T15:29:00.214Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "itsdangerous"
|
||||
version = "2.2.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/9c/cb/8ac0172223afbccb63986cc25049b154ecfb5e85932587206f42317be31d/itsdangerous-2.2.0.tar.gz", hash = "sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173", size = 54410, upload-time = "2024-04-16T21:28:15.614Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/96/92447566d16df59b2a776c0fb82dbc4d9e07cd95062562af01e408583fc4/itsdangerous-2.2.0-py3-none-any.whl", hash = "sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef", size = 16234, upload-time = "2024-04-16T21:28:14.499Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jinja2"
|
||||
version = "3.1.6"
|
||||
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{ url = "https://files.pythonhosted.org/packages/cc/f5/1b5d88e5a65168b435e8339b53d027e3e7adecb0c7d157bc86d18f78471b/xgboost-3.0.2-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:d534242489265621397ff403bb1c6235d2e6c66938639239fdf2d6b39d27e339", size = 253887220, upload-time = "2025-05-25T09:10:24.541Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/29/22/e3ff2dfafe862a91733dfa0aecdb4794aa1d9a18e09a14e118bde0cbc2db/xgboost-3.0.2-py3-none-win_amd64.whl", hash = "sha256:b4c89b71d134da9fa6318e3c9f5459317d1013b4d57059d10ed2840750e2f7e1", size = 149974575, upload-time = "2025-05-25T09:11:23.554Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.22.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/12/b6/7b3d16792fdf94f146bed92be90b4eb4563569eca91513c8609aebf0c167/zipp-3.22.0.tar.gz", hash = "sha256:dd2f28c3ce4bc67507bfd3781d21b7bb2be31103b51a4553ad7d90b84e57ace5", size = 25257, upload-time = "2025-05-26T14:46:32.217Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ad/da/f64669af4cae46f17b90798a827519ce3737d31dbafad65d391e49643dc4/zipp-3.22.0-py3-none-any.whl", hash = "sha256:fe208f65f2aca48b81f9e6fd8cf7b8b32c26375266b009b413d45306b6148343", size = 9796, upload-time = "2025-05-26T14:46:30.775Z" },
|
||||
]
|
||||
|
||||
39
xgboost/custom_xgboost.py
Normal file
39
xgboost/custom_xgboost.py
Normal file
@@ -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)
|
||||
731
xgboost/main.py
Normal file
731
xgboost/main.py
Normal file
@@ -0,0 +1,731 @@
|
||||
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 sklearn.model_selection import train_test_split
|
||||
from custom_xgboost import CustomXGBoostGPU
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from plot_results import display_actual_vs_predicted, plot_target_distribution, plot_predicted_vs_actual_log_returns, plot_predicted_vs_actual_prices
|
||||
import ta
|
||||
from cycles.supertrend import Supertrends
|
||||
from ta.trend import SMAIndicator, DPOIndicator, IchimokuIndicator, PSARIndicator
|
||||
from ta.momentum import ROCIndicator, KAMAIndicator, UltimateOscillator, StochasticOscillator, WilliamsRIndicator
|
||||
from ta.volatility import KeltnerChannel, DonchianChannel
|
||||
from ta.others import DailyReturnIndicator
|
||||
import time
|
||||
from numba import njit
|
||||
|
||||
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
|
||||
|
||||
if __name__ == '__main__':
|
||||
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 jobs for lags, rolling stats, log returns, and volatility
|
||||
feature_jobs = []
|
||||
# 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'Adding lag feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_lag, col, lag))
|
||||
# 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'Adding rolling stat feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_rolling, col, stat, window))
|
||||
# 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'Adding log return feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_log_return, horizon))
|
||||
# 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'Adding volatility feature job: {feature_name}')
|
||||
feature_jobs.append((feature_name, compute_volatility, window))
|
||||
|
||||
# Sequential computation for all non-cached features
|
||||
if feature_jobs:
|
||||
print(f'Computing {len(feature_jobs)} features sequentially...')
|
||||
for job in feature_jobs:
|
||||
print(f'Computing feature job: {job[0]}')
|
||||
feature_name, result = run_feature_job(job, df)
|
||||
features_dict[feature_name] = result
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved computed feature: {feature_file}')
|
||||
print('All features computed.')
|
||||
else:
|
||||
print('All features loaded from cache.')
|
||||
|
||||
# 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
|
||||
|
||||
# Drop intermediate features_df to free memory
|
||||
print('Dropping intermediate features_df to free memory...')
|
||||
del features_df
|
||||
import gc
|
||||
gc.collect()
|
||||
|
||||
feature_end_time = time.time()
|
||||
print(f'Feature computation completed in {feature_end_time - feature_start_time:.2f} seconds.')
|
||||
|
||||
# Add Supertrend indicators (custom)
|
||||
print('Preparing data for Supertrend calculation...')
|
||||
st_df = df.rename(columns={'High': 'high', 'Low': 'low', 'Close': 'close'})
|
||||
|
||||
print('Calculating Supertrend indicators...')
|
||||
supertrend = Supertrends(st_df)
|
||||
st_results = supertrend.calculate_supertrend_indicators()
|
||||
for idx, st in enumerate(st_results):
|
||||
period = st['params']['period']
|
||||
multiplier = st['params']['multiplier']
|
||||
# Skip useless supertrend features
|
||||
if (period == 10 and multiplier == 1.0) or (period == 11 and multiplier == 2.0):
|
||||
continue
|
||||
print(f'Adding Supertrend features: supertrend_{period}_{multiplier} and supertrend_trend_{period}_{multiplier}')
|
||||
df[f'supertrend_{period}_{multiplier}'] = st['results']['supertrend']
|
||||
df[f'supertrend_trend_{period}_{multiplier}'] = st['results']['trend']
|
||||
|
||||
# Add time features (exclude 'dayofweek')
|
||||
print('Adding hour feature...')
|
||||
df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
|
||||
df['hour'] = df['Timestamp'].dt.hour
|
||||
|
||||
# Drop NaNs after all feature engineering
|
||||
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 the features used for training
|
||||
print("Features used for training:", feature_cols)
|
||||
|
||||
# Drop excluded columns to save memory
|
||||
print('Dropping excluded columns to save memory...')
|
||||
df = df[feature_cols + ['log_return', 'Timestamp']]
|
||||
|
||||
print('Preparing X and y...')
|
||||
X = df[feature_cols].values.astype(np.float32)
|
||||
y = df['log_return'].values.astype(np.float32)
|
||||
|
||||
split_idx = int(len(X) * 0.8)
|
||||
print(f'Splitting data: {split_idx} train, {len(X) - split_idx} test')
|
||||
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:]
|
||||
|
||||
print('Initializing model...')
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
|
||||
print('Training model...')
|
||||
booster = model.train()
|
||||
|
||||
print('Training complete.')
|
||||
|
||||
# Save the trained model
|
||||
model.save_model('./data/xgboost_model.json')
|
||||
print('Model saved to ./data/xgboost_model.json')
|
||||
|
||||
if hasattr(model, 'params'):
|
||||
print("Model hyperparameters:", model.params)
|
||||
if hasattr(model, 'model') and hasattr(model.model, 'get_score'):
|
||||
import operator
|
||||
importances = model.model.get_score(importance_type='weight')
|
||||
# Map f0, f1, ... to actual feature names
|
||||
feature_map = {f"f{idx}": name for idx, name in enumerate(feature_cols)}
|
||||
sorted_importances = sorted(importances.items(), key=operator.itemgetter(1), reverse=True)
|
||||
print('Feature importances (sorted, with names):')
|
||||
for feat, score in sorted_importances:
|
||||
print(f'{feature_map.get(feat, feat)}: {score}')
|
||||
|
||||
print('Making predictions for first 5 test samples...')
|
||||
preds = model.predict(X_test[:5])
|
||||
print('Predictions for first 5 test samples:', preds)
|
||||
print('Actual values for first 5 test samples:', y_test[:5])
|
||||
|
||||
print('Making predictions for all test samples...')
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
print(f'RMSE on test set: {rmse:.4f}')
|
||||
|
||||
print('Saving y_test and test_preds to disk...')
|
||||
np.save('./data/y_test.npy', y_test)
|
||||
np.save('./data/test_preds.npy', test_preds)
|
||||
|
||||
# Reconstruct price series from log returns
|
||||
print('Reconstructing price series from log returns...')
|
||||
# Get the last available Close price before the test set
|
||||
# The DataFrame df has been reset, so use split_idx to get the right row
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
# Reload original CSV to get Close prices if not present
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx] # This is the price at the split point
|
||||
# Actual prices
|
||||
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
|
||||
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:])
|
||||
|
||||
print('Plotting predicted vs actual prices...')
|
||||
plot_predicted_vs_actual_prices(actual_prices, predicted_prices, test_timestamps)
|
||||
|
||||
print("Final features used for training:", feature_cols)
|
||||
|
||||
print("Shape of X:", X.shape)
|
||||
print("First row of X:", X[0])
|
||||
print("stoch_k in feature_cols?", "stoch_k" in feature_cols)
|
||||
if "stoch_k" in feature_cols:
|
||||
idx = feature_cols.index("stoch_k")
|
||||
print("First 10 values of stoch_k:", X[:10, idx])
|
||||
169
xgboost/plot_results.py
Normal file
169
xgboost/plot_results.py
Normal file
@@ -0,0 +1,169 @@
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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='log_return_line_plot.html')
|
||||
|
||||
# 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='log_return_scatter_plot.html')
|
||||
|
||||
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='price_line_plot.html')
|
||||
|
||||
# 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='price_scatter_plot.html')
|
||||
Reference in New Issue
Block a user