init
This commit is contained in:
Simon Moisy
parent
4bad7c365c
commit
8d3f045a92
1
.python-version
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1
.python-version
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3.12
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39
custom_xgboost.py
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39
custom_xgboost.py
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import xgboost as xgb
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import numpy as np
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class CustomXGBoostGPU:
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def __init__(self, X_train, X_test, y_train, y_test):
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self.X_train = X_train.astype(np.float32)
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self.X_test = X_test.astype(np.float32)
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self.y_train = y_train.astype(np.float32)
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self.y_test = y_test.astype(np.float32)
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self.model = None
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self.params = None # Will be set during training
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def train(self, **xgb_params):
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params = {
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'tree_method': 'hist',
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'device': 'cuda',
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse',
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'verbosity': 1,
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}
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params.update(xgb_params)
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self.params = params # Store params for later access
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dtrain = xgb.DMatrix(self.X_train, label=self.y_train)
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dtest = xgb.DMatrix(self.X_test, label=self.y_test)
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evals = [(dtrain, 'train'), (dtest, 'eval')]
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self.model = xgb.train(params, dtrain, num_boost_round=100, evals=evals, early_stopping_rounds=10)
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return self.model
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def predict(self, X):
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if self.model is None:
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raise ValueError('Model not trained yet.')
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dmatrix = xgb.DMatrix(X.astype(np.float32))
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return self.model.predict(dmatrix)
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def save_model(self, file_path):
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"""Save the trained XGBoost model to the specified file path."""
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if self.model is None:
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raise ValueError('Model not trained yet.')
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self.model.save_model(file_path)
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804
main.py
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main.py
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import sys
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import os
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import pandas as pd
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import numpy as np
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from custom_xgboost import CustomXGBoostGPU
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from plot_results import plot_prediction_error_distribution, plot_direction_transition_heatmap
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import time
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from numba import njit
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import csv
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import ta
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def run_indicator(func, *args):
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return func(*args)
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def run_indicator_job(job):
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import time
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func, *args = job
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indicator_name = func.__name__
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start = time.time()
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result = func(*args)
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elapsed = time.time() - start
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print(f'Indicator {indicator_name} computed in {elapsed:.4f} seconds')
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return result
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def calc_rsi(close):
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from ta.momentum import RSIIndicator
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return ('rsi', RSIIndicator(close, window=14).rsi())
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def calc_macd(close):
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from ta.trend import MACD
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return ('macd', MACD(close).macd())
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def calc_bollinger(close):
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from ta.volatility import BollingerBands
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bb = BollingerBands(close=close, window=20, window_dev=2)
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return [
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('bb_bbm', bb.bollinger_mavg()),
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('bb_bbh', bb.bollinger_hband()),
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('bb_bbl', bb.bollinger_lband()),
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('bb_bb_width', bb.bollinger_hband() - bb.bollinger_lband())
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]
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def calc_stochastic(high, low, close):
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from ta.momentum import StochasticOscillator
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stoch = StochasticOscillator(high=high, low=low, close=close, window=14, smooth_window=3)
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return [
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('stoch_k', stoch.stoch()),
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('stoch_d', stoch.stoch_signal())
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]
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def calc_atr(high, low, close):
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from ta.volatility import AverageTrueRange
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atr = AverageTrueRange(high=high, low=low, close=close, window=14)
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return ('atr', atr.average_true_range())
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def calc_cci(high, low, close):
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from ta.trend import CCIIndicator
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cci = CCIIndicator(high=high, low=low, close=close, window=20)
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return ('cci', cci.cci())
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def calc_williamsr(high, low, close):
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from ta.momentum import WilliamsRIndicator
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willr = WilliamsRIndicator(high=high, low=low, close=close, lbp=14)
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return ('williams_r', willr.williams_r())
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def calc_ema(close):
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from ta.trend import EMAIndicator
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ema = EMAIndicator(close=close, window=14)
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return ('ema_14', ema.ema_indicator())
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def calc_obv(close, volume):
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from ta.volume import OnBalanceVolumeIndicator
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obv = OnBalanceVolumeIndicator(close=close, volume=volume)
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return ('obv', obv.on_balance_volume())
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def calc_cmf(high, low, close, volume):
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from ta.volume import ChaikinMoneyFlowIndicator
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cmf = ChaikinMoneyFlowIndicator(high=high, low=low, close=close, volume=volume, window=20)
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return ('cmf', cmf.chaikin_money_flow())
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def calc_sma(close):
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from ta.trend import SMAIndicator
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return [
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('sma_50', SMAIndicator(close, window=50).sma_indicator()),
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('sma_200', SMAIndicator(close, window=200).sma_indicator())
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]
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def calc_roc(close):
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from ta.momentum import ROCIndicator
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return ('roc_10', ROCIndicator(close, window=10).roc())
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def calc_momentum(close):
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return ('momentum_10', close - close.shift(10))
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def calc_psar(high, low, close):
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# Use the Numba-accelerated fast_psar function for speed
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psar_values = fast_psar(np.array(high), np.array(low), np.array(close))
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return [('psar', pd.Series(psar_values, index=close.index))]
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def calc_donchian(high, low, close):
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from ta.volatility import DonchianChannel
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donchian = DonchianChannel(high, low, close, window=20)
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return [
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('donchian_hband', donchian.donchian_channel_hband()),
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('donchian_lband', donchian.donchian_channel_lband()),
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('donchian_mband', donchian.donchian_channel_mband())
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]
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def calc_keltner(high, low, close):
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from ta.volatility import KeltnerChannel
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keltner = KeltnerChannel(high, low, close, window=20)
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return [
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('keltner_hband', keltner.keltner_channel_hband()),
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('keltner_lband', keltner.keltner_channel_lband()),
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('keltner_mband', keltner.keltner_channel_mband())
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]
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def calc_dpo(close):
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from ta.trend import DPOIndicator
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return ('dpo_20', DPOIndicator(close, window=20).dpo())
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def calc_ultimate(high, low, close):
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from ta.momentum import UltimateOscillator
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return ('ultimate_osc', UltimateOscillator(high, low, close).ultimate_oscillator())
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def calc_ichimoku(high, low):
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from ta.trend import IchimokuIndicator
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ichimoku = IchimokuIndicator(high, low, window1=9, window2=26, window3=52)
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return [
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('ichimoku_a', ichimoku.ichimoku_a()),
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('ichimoku_b', ichimoku.ichimoku_b()),
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('ichimoku_base_line', ichimoku.ichimoku_base_line()),
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('ichimoku_conversion_line', ichimoku.ichimoku_conversion_line())
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]
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def calc_elder_ray(close, low, high):
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from ta.trend import EMAIndicator
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ema = EMAIndicator(close, window=13).ema_indicator()
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return [
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('elder_ray_bull', ema - low),
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('elder_ray_bear', ema - high)
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]
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def calc_daily_return(close):
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from ta.others import DailyReturnIndicator
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return ('daily_return', DailyReturnIndicator(close).daily_return())
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@njit
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def fast_psar(high, low, close, af=0.02, max_af=0.2):
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length = len(close)
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psar = np.zeros(length)
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bull = True
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af_step = af
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ep = low[0]
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psar[0] = low[0]
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for i in range(1, length):
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prev_psar = psar[i-1]
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if bull:
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psar[i] = prev_psar + af_step * (ep - prev_psar)
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if low[i] < psar[i]:
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bull = False
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psar[i] = ep
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af_step = af
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ep = low[i]
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else:
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if high[i] > ep:
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ep = high[i]
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af_step = min(af_step + af, max_af)
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else:
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psar[i] = prev_psar + af_step * (ep - prev_psar)
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if high[i] > psar[i]:
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bull = True
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psar[i] = ep
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af_step = af
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ep = high[i]
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else:
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if low[i] < ep:
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ep = low[i]
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af_step = min(af_step + af, max_af)
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return psar
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def compute_lag(df, col, lag):
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return df[col].shift(lag)
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def compute_rolling(df, col, stat, window):
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if stat == 'mean':
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return df[col].rolling(window).mean()
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elif stat == 'std':
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return df[col].rolling(window).std()
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elif stat == 'min':
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return df[col].rolling(window).min()
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elif stat == 'max':
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return df[col].rolling(window).max()
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def compute_log_return(df, horizon):
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return np.log(df['Close'] / df['Close'].shift(horizon))
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def compute_volatility(df, window):
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return df['log_return'].rolling(window).std()
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def run_feature_job(job, df):
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feature_name, func, *args = job
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print(f'Computing feature: {feature_name}')
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result = func(df, *args)
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return feature_name, result
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def calc_adx(high, low, close):
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from ta.trend import ADXIndicator
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adx = ADXIndicator(high=high, low=low, close=close, window=14)
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return [
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('adx', adx.adx()),
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('adx_pos', adx.adx_pos()),
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('adx_neg', adx.adx_neg())
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]
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def calc_trix(close):
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from ta.trend import TRIXIndicator
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trix = TRIXIndicator(close=close, window=15)
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return ('trix', trix.trix())
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def calc_vortex(high, low, close):
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from ta.trend import VortexIndicator
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vortex = VortexIndicator(high=high, low=low, close=close, window=14)
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return [
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('vortex_pos', vortex.vortex_indicator_pos()),
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('vortex_neg', vortex.vortex_indicator_neg())
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]
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def calc_kama(close):
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import pandas_ta as ta
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kama = ta.kama(close, length=10)
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return ('kama', kama)
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def calc_force_index(close, volume):
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from ta.volume import ForceIndexIndicator
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fi = ForceIndexIndicator(close=close, volume=volume, window=13)
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return ('force_index', fi.force_index())
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def calc_eom(high, low, volume):
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from ta.volume import EaseOfMovementIndicator
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eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
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return ('eom', eom.ease_of_movement())
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def calc_mfi(high, low, close, volume):
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from ta.volume import MFIIndicator
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mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
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return ('mfi', mfi.money_flow_index())
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def calc_adi(high, low, close, volume):
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from ta.volume import AccDistIndexIndicator
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adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
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return ('adi', adi.acc_dist_index())
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def calc_tema(close):
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import pandas_ta as ta
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tema = ta.tema(close, length=10)
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return ('tema', tema)
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def calc_stochrsi(close):
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from ta.momentum import StochRSIIndicator
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stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
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return [
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('stochrsi', stochrsi.stochrsi()),
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('stochrsi_k', stochrsi.stochrsi_k()),
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('stochrsi_d', stochrsi.stochrsi_d())
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]
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def calc_awesome_oscillator(high, low):
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from ta.momentum import AwesomeOscillatorIndicator
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ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
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return ('awesome_osc', ao.awesome_oscillator())
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if __name__ == '__main__':
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IMPUTE_NANS = True # Set to True to impute NaNs, False to drop rows with NaNs
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csv_path = '../data/btcusd_1-min_data.csv'
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csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
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print('Reading CSV and filtering data...')
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df = pd.read_csv(csv_path)
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df = df[df['Volume'] != 0]
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min_date = '2017-06-01'
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print('Converting Timestamp and filtering by date...')
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df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
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df = df[df['Timestamp'] >= min_date]
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lags = 3
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print('Calculating log returns as the new target...')
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df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
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ohlcv_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
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window_sizes = [5, 15, 30] # in minutes, adjust as needed
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features_dict = {}
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print('Starting feature computation...')
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feature_start_time = time.time()
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# --- Technical Indicator Features: Calculate or Load from Cache ---
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print('Calculating or loading technical indicator features...')
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# RSI
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feature_file = f'../data/{csv_prefix}_rsi.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['rsi'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: rsi')
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_, values = calc_rsi(df['Close'])
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features_dict['rsi'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# MACD
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feature_file = f'../data/{csv_prefix}_macd.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['macd'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: macd')
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_, values = calc_macd(df['Close'])
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features_dict['macd'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# ATR
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feature_file = f'../data/{csv_prefix}_atr.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['atr'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: atr')
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_, values = calc_atr(df['High'], df['Low'], df['Close'])
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features_dict['atr'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# CCI
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feature_file = f'../data/{csv_prefix}_cci.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['cci'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: cci')
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_, values = calc_cci(df['High'], df['Low'], df['Close'])
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features_dict['cci'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# Williams %R
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feature_file = f'../data/{csv_prefix}_williams_r.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['williams_r'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: williams_r')
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_, values = calc_williamsr(df['High'], df['Low'], df['Close'])
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features_dict['williams_r'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# EMA 14
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feature_file = f'../data/{csv_prefix}_ema_14.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['ema_14'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: ema_14')
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_, values = calc_ema(df['Close'])
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features_dict['ema_14'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# OBV
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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)
|
||||
318
plot_results.py
Normal file
318
plot_results.py
Normal file
@ -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)
|
||||
|
||||
14
pyproject.toml
Normal file
14
pyproject.toml
Normal file
@ -0,0 +1,14 @@
|
||||
[project]
|
||||
name = "ohlcvpredictor"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = [
|
||||
"dash>=3.0.4",
|
||||
"numba>=0.61.2",
|
||||
"pandas>=2.2.3",
|
||||
"scikit-learn>=1.6.1",
|
||||
"ta>=0.11.0",
|
||||
"xgboost>=3.0.2",
|
||||
]
|
||||
37
requirements.txt
Normal file
37
requirements.txt
Normal file
@ -0,0 +1,37 @@
|
||||
blinker==1.9.0
|
||||
certifi==2025.4.26
|
||||
charset-normalizer==3.4.2
|
||||
click==8.2.1
|
||||
dash==3.0.4
|
||||
flask==3.0.3
|
||||
idna==3.10
|
||||
importlib-metadata==8.7.0
|
||||
itsdangerous==2.2.0
|
||||
jinja2==3.1.6
|
||||
joblib==1.5.1
|
||||
llvmlite==0.44.0
|
||||
markupsafe==3.0.2
|
||||
narwhals==1.41.0
|
||||
nest-asyncio==1.6.0
|
||||
numba==0.61.2
|
||||
numpy==2.2.6
|
||||
nvidia-nccl-cu12==2.26.5
|
||||
packaging==25.0
|
||||
pandas==2.2.3
|
||||
plotly==6.1.2
|
||||
python-dateutil==2.9.0.post0
|
||||
pytz==2025.2
|
||||
requests==2.32.3
|
||||
retrying==1.3.4
|
||||
scikit-learn==1.6.1
|
||||
scipy==1.15.3
|
||||
setuptools==80.9.0
|
||||
six==1.17.0
|
||||
ta==0.11.0
|
||||
threadpoolctl==3.6.0
|
||||
typing-extensions==4.13.2
|
||||
tzdata==2025.2
|
||||
urllib3==2.4.0
|
||||
werkzeug==3.0.6
|
||||
xgboost==3.0.2
|
||||
zipp==3.22.0
|
||||
605
uv.lock
generated
Normal file
605
uv.lock
generated
Normal file
@ -0,0 +1,605 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[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 = [
|
||||
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Reference in New Issue
Block a user