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a877f14e65
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de67b27e37 |
4
.gitignore
vendored
4
.gitignore
vendored
@@ -1,11 +1,13 @@
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||||
# ---> Python
|
||||
*.json
|
||||
/data/*.db
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/credentials/*.json
|
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*.csv
|
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*.png
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.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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||||
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||||
@@ -1 +0,0 @@
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3.10
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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
@@ -5,10 +5,15 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.10"
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dependencies = [
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"dash>=3.0.4",
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"numba>=0.61.2",
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"psutil>=7.0.0",
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"scipy>=1.15.3",
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"seaborn>=0.13.2",
|
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"ta>=0.11.0",
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"xgboost>=3.0.2",
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]
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BIN
requirements.txt
BIN
requirements.txt
Binary file not shown.
404
uv.lock
generated
404
uv.lock
generated
@@ -7,6 +7,15 @@ resolution-markers = [
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"python_full_version < '3.11'",
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@@ -167,22 +197,68 @@ name = "cycles"
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{ name = "xgboost" },
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[package.metadata]
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requires-dist = [
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{ name = "ta", specifier = ">=0.11.0" },
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{ name = "importlib-metadata" },
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{ name = "nest-asyncio" },
|
||||
{ name = "plotly" },
|
||||
{ name = "requests" },
|
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{ name = "retrying" },
|
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{ name = "setuptools" },
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{ name = "typing-extensions" },
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||||
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||||
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||||
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||||
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||||
[[package]]
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||||
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||||
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||||
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||||
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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)
|
||||
806
xgboost/main.py
Normal file
806
xgboost/main.py
Normal file
@@ -0,0 +1,806 @@
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from custom_xgboost import CustomXGBoostGPU
|
||||
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
|
||||
from plot_results import plot_prediction_error_distribution, plot_direction_transition_heatmap
|
||||
from cycles.supertrend import Supertrends
|
||||
import time
|
||||
from numba import njit
|
||||
import itertools
|
||||
import csv
|
||||
import pandas_ta as ta
|
||||
|
||||
def run_indicator(func, *args):
|
||||
return func(*args)
|
||||
|
||||
def run_indicator_job(job):
|
||||
import time
|
||||
func, *args = job
|
||||
indicator_name = func.__name__
|
||||
start = time.time()
|
||||
result = func(*args)
|
||||
elapsed = time.time() - start
|
||||
print(f'Indicator {indicator_name} computed in {elapsed:.4f} seconds')
|
||||
return result
|
||||
|
||||
def calc_rsi(close):
|
||||
from ta.momentum import RSIIndicator
|
||||
return ('rsi', RSIIndicator(close, window=14).rsi())
|
||||
|
||||
def calc_macd(close):
|
||||
from ta.trend import MACD
|
||||
return ('macd', MACD(close).macd())
|
||||
|
||||
def calc_bollinger(close):
|
||||
from ta.volatility import BollingerBands
|
||||
bb = BollingerBands(close=close, window=20, window_dev=2)
|
||||
return [
|
||||
('bb_bbm', bb.bollinger_mavg()),
|
||||
('bb_bbh', bb.bollinger_hband()),
|
||||
('bb_bbl', bb.bollinger_lband()),
|
||||
('bb_bb_width', bb.bollinger_hband() - bb.bollinger_lband())
|
||||
]
|
||||
|
||||
def calc_stochastic(high, low, close):
|
||||
from ta.momentum import StochasticOscillator
|
||||
stoch = StochasticOscillator(high=high, low=low, close=close, window=14, smooth_window=3)
|
||||
return [
|
||||
('stoch_k', stoch.stoch()),
|
||||
('stoch_d', stoch.stoch_signal())
|
||||
]
|
||||
|
||||
def calc_atr(high, low, close):
|
||||
from ta.volatility import AverageTrueRange
|
||||
atr = AverageTrueRange(high=high, low=low, close=close, window=14)
|
||||
return ('atr', atr.average_true_range())
|
||||
|
||||
def calc_cci(high, low, close):
|
||||
from ta.trend import CCIIndicator
|
||||
cci = CCIIndicator(high=high, low=low, close=close, window=20)
|
||||
return ('cci', cci.cci())
|
||||
|
||||
def calc_williamsr(high, low, close):
|
||||
from ta.momentum import WilliamsRIndicator
|
||||
willr = WilliamsRIndicator(high=high, low=low, close=close, lbp=14)
|
||||
return ('williams_r', willr.williams_r())
|
||||
|
||||
def calc_ema(close):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close=close, window=14)
|
||||
return ('ema_14', ema.ema_indicator())
|
||||
|
||||
def calc_obv(close, volume):
|
||||
from ta.volume import OnBalanceVolumeIndicator
|
||||
obv = OnBalanceVolumeIndicator(close=close, volume=volume)
|
||||
return ('obv', obv.on_balance_volume())
|
||||
|
||||
def calc_cmf(high, low, close, volume):
|
||||
from ta.volume import ChaikinMoneyFlowIndicator
|
||||
cmf = ChaikinMoneyFlowIndicator(high=high, low=low, close=close, volume=volume, window=20)
|
||||
return ('cmf', cmf.chaikin_money_flow())
|
||||
|
||||
def calc_sma(close):
|
||||
from ta.trend import SMAIndicator
|
||||
return [
|
||||
('sma_50', SMAIndicator(close, window=50).sma_indicator()),
|
||||
('sma_200', SMAIndicator(close, window=200).sma_indicator())
|
||||
]
|
||||
|
||||
def calc_roc(close):
|
||||
from ta.momentum import ROCIndicator
|
||||
return ('roc_10', ROCIndicator(close, window=10).roc())
|
||||
|
||||
def calc_momentum(close):
|
||||
return ('momentum_10', close - close.shift(10))
|
||||
|
||||
def calc_psar(high, low, close):
|
||||
# Use the Numba-accelerated fast_psar function for speed
|
||||
psar_values = fast_psar(np.array(high), np.array(low), np.array(close))
|
||||
return [('psar', pd.Series(psar_values, index=close.index))]
|
||||
|
||||
def calc_donchian(high, low, close):
|
||||
from ta.volatility import DonchianChannel
|
||||
donchian = DonchianChannel(high, low, close, window=20)
|
||||
return [
|
||||
('donchian_hband', donchian.donchian_channel_hband()),
|
||||
('donchian_lband', donchian.donchian_channel_lband()),
|
||||
('donchian_mband', donchian.donchian_channel_mband())
|
||||
]
|
||||
|
||||
def calc_keltner(high, low, close):
|
||||
from ta.volatility import KeltnerChannel
|
||||
keltner = KeltnerChannel(high, low, close, window=20)
|
||||
return [
|
||||
('keltner_hband', keltner.keltner_channel_hband()),
|
||||
('keltner_lband', keltner.keltner_channel_lband()),
|
||||
('keltner_mband', keltner.keltner_channel_mband())
|
||||
]
|
||||
|
||||
def calc_dpo(close):
|
||||
from ta.trend import DPOIndicator
|
||||
return ('dpo_20', DPOIndicator(close, window=20).dpo())
|
||||
|
||||
def calc_ultimate(high, low, close):
|
||||
from ta.momentum import UltimateOscillator
|
||||
return ('ultimate_osc', UltimateOscillator(high, low, close).ultimate_oscillator())
|
||||
|
||||
def calc_ichimoku(high, low):
|
||||
from ta.trend import IchimokuIndicator
|
||||
ichimoku = IchimokuIndicator(high, low, window1=9, window2=26, window3=52)
|
||||
return [
|
||||
('ichimoku_a', ichimoku.ichimoku_a()),
|
||||
('ichimoku_b', ichimoku.ichimoku_b()),
|
||||
('ichimoku_base_line', ichimoku.ichimoku_base_line()),
|
||||
('ichimoku_conversion_line', ichimoku.ichimoku_conversion_line())
|
||||
]
|
||||
|
||||
def calc_elder_ray(close, low, high):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close, window=13).ema_indicator()
|
||||
return [
|
||||
('elder_ray_bull', ema - low),
|
||||
('elder_ray_bear', ema - high)
|
||||
]
|
||||
|
||||
def calc_daily_return(close):
|
||||
from ta.others import DailyReturnIndicator
|
||||
return ('daily_return', DailyReturnIndicator(close).daily_return())
|
||||
|
||||
@njit
|
||||
def fast_psar(high, low, close, af=0.02, max_af=0.2):
|
||||
length = len(close)
|
||||
psar = np.zeros(length)
|
||||
bull = True
|
||||
af_step = af
|
||||
ep = low[0]
|
||||
psar[0] = low[0]
|
||||
for i in range(1, length):
|
||||
prev_psar = psar[i-1]
|
||||
if bull:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if low[i] < psar[i]:
|
||||
bull = False
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = low[i]
|
||||
else:
|
||||
if high[i] > ep:
|
||||
ep = high[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
else:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if high[i] > psar[i]:
|
||||
bull = True
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = high[i]
|
||||
else:
|
||||
if low[i] < ep:
|
||||
ep = low[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
return psar
|
||||
|
||||
def compute_lag(df, col, lag):
|
||||
return df[col].shift(lag)
|
||||
|
||||
def compute_rolling(df, col, stat, window):
|
||||
if stat == 'mean':
|
||||
return df[col].rolling(window).mean()
|
||||
elif stat == 'std':
|
||||
return df[col].rolling(window).std()
|
||||
elif stat == 'min':
|
||||
return df[col].rolling(window).min()
|
||||
elif stat == 'max':
|
||||
return df[col].rolling(window).max()
|
||||
|
||||
def compute_log_return(df, horizon):
|
||||
return np.log(df['Close'] / df['Close'].shift(horizon))
|
||||
|
||||
def compute_volatility(df, window):
|
||||
return df['log_return'].rolling(window).std()
|
||||
|
||||
def run_feature_job(job, df):
|
||||
feature_name, func, *args = job
|
||||
print(f'Computing feature: {feature_name}')
|
||||
result = func(df, *args)
|
||||
return feature_name, result
|
||||
|
||||
def calc_adx(high, low, close):
|
||||
from ta.trend import ADXIndicator
|
||||
adx = ADXIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('adx', adx.adx()),
|
||||
('adx_pos', adx.adx_pos()),
|
||||
('adx_neg', adx.adx_neg())
|
||||
]
|
||||
|
||||
def calc_trix(close):
|
||||
from ta.trend import TRIXIndicator
|
||||
trix = TRIXIndicator(close=close, window=15)
|
||||
return ('trix', trix.trix())
|
||||
|
||||
def calc_vortex(high, low, close):
|
||||
from ta.trend import VortexIndicator
|
||||
vortex = VortexIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('vortex_pos', vortex.vortex_indicator_pos()),
|
||||
('vortex_neg', vortex.vortex_indicator_neg())
|
||||
]
|
||||
|
||||
def calc_kama(close):
|
||||
import pandas_ta as ta
|
||||
kama = ta.kama(close, length=10)
|
||||
return ('kama', kama)
|
||||
|
||||
def calc_force_index(close, volume):
|
||||
from ta.volume import ForceIndexIndicator
|
||||
fi = ForceIndexIndicator(close=close, volume=volume, window=13)
|
||||
return ('force_index', fi.force_index())
|
||||
|
||||
def calc_eom(high, low, volume):
|
||||
from ta.volume import EaseOfMovementIndicator
|
||||
eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
|
||||
return ('eom', eom.ease_of_movement())
|
||||
|
||||
def calc_mfi(high, low, close, volume):
|
||||
from ta.volume import MFIIndicator
|
||||
mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
|
||||
return ('mfi', mfi.money_flow_index())
|
||||
|
||||
def calc_adi(high, low, close, volume):
|
||||
from ta.volume import AccDistIndexIndicator
|
||||
adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
|
||||
return ('adi', adi.acc_dist_index())
|
||||
|
||||
def calc_tema(close):
|
||||
import pandas_ta as ta
|
||||
tema = ta.tema(close, length=10)
|
||||
return ('tema', tema)
|
||||
|
||||
def calc_stochrsi(close):
|
||||
from ta.momentum import StochRSIIndicator
|
||||
stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
|
||||
return [
|
||||
('stochrsi', stochrsi.stochrsi()),
|
||||
('stochrsi_k', stochrsi.stochrsi_k()),
|
||||
('stochrsi_d', stochrsi.stochrsi_d())
|
||||
]
|
||||
|
||||
def calc_awesome_oscillator(high, low):
|
||||
from ta.momentum import AwesomeOscillatorIndicator
|
||||
ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
|
||||
return ('awesome_osc', ao.awesome_oscillator())
|
||||
|
||||
if __name__ == '__main__':
|
||||
IMPUTE_NANS = True # Set to True to impute NaNs, False to drop rows with NaNs
|
||||
csv_path = './data/btcusd_1-min_data.csv'
|
||||
csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
|
||||
|
||||
print('Reading CSV and filtering data...')
|
||||
df = pd.read_csv(csv_path)
|
||||
df = df[df['Volume'] != 0]
|
||||
|
||||
min_date = '2017-06-01'
|
||||
print('Converting Timestamp and filtering by date...')
|
||||
df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
|
||||
df = df[df['Timestamp'] >= min_date]
|
||||
|
||||
lags = 3
|
||||
|
||||
print('Calculating log returns as the new target...')
|
||||
df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
|
||||
|
||||
ohlcv_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
|
||||
window_sizes = [5, 15, 30] # in minutes, adjust as needed
|
||||
|
||||
features_dict = {}
|
||||
|
||||
print('Starting feature computation...')
|
||||
feature_start_time = time.time()
|
||||
|
||||
# --- Technical Indicator Features: Calculate or Load from Cache ---
|
||||
print('Calculating or loading technical indicator features...')
|
||||
# RSI
|
||||
feature_file = f'./data/{csv_prefix}_rsi.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['rsi'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: rsi')
|
||||
_, values = calc_rsi(df['Close'])
|
||||
features_dict['rsi'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# MACD
|
||||
feature_file = f'./data/{csv_prefix}_macd.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['macd'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: macd')
|
||||
_, values = calc_macd(df['Close'])
|
||||
features_dict['macd'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# ATR
|
||||
feature_file = f'./data/{csv_prefix}_atr.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['atr'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: atr')
|
||||
_, values = calc_atr(df['High'], df['Low'], df['Close'])
|
||||
features_dict['atr'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# CCI
|
||||
feature_file = f'./data/{csv_prefix}_cci.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['cci'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: cci')
|
||||
_, values = calc_cci(df['High'], df['Low'], df['Close'])
|
||||
features_dict['cci'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Williams %R
|
||||
feature_file = f'./data/{csv_prefix}_williams_r.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['williams_r'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: williams_r')
|
||||
_, values = calc_williamsr(df['High'], df['Low'], df['Close'])
|
||||
features_dict['williams_r'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# EMA 14
|
||||
feature_file = f'./data/{csv_prefix}_ema_14.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['ema_14'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: ema_14')
|
||||
_, values = calc_ema(df['Close'])
|
||||
features_dict['ema_14'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# OBV
|
||||
feature_file = f'./data/{csv_prefix}_obv.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['obv'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: obv')
|
||||
_, values = calc_obv(df['Close'], df['Volume'])
|
||||
features_dict['obv'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# CMF
|
||||
feature_file = f'./data/{csv_prefix}_cmf.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['cmf'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: cmf')
|
||||
_, values = calc_cmf(df['High'], df['Low'], df['Close'], df['Volume'])
|
||||
features_dict['cmf'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# ROC 10
|
||||
feature_file = f'./data/{csv_prefix}_roc_10.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['roc_10'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: roc_10')
|
||||
_, values = calc_roc(df['Close'])
|
||||
features_dict['roc_10'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# DPO 20
|
||||
feature_file = f'./data/{csv_prefix}_dpo_20.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['dpo_20'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: dpo_20')
|
||||
_, values = calc_dpo(df['Close'])
|
||||
features_dict['dpo_20'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Ultimate Oscillator
|
||||
feature_file = f'./data/{csv_prefix}_ultimate_osc.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['ultimate_osc'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: ultimate_osc')
|
||||
_, values = calc_ultimate(df['High'], df['Low'], df['Close'])
|
||||
features_dict['ultimate_osc'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Daily Return
|
||||
feature_file = f'./data/{csv_prefix}_daily_return.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'A Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['daily_return'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: daily_return')
|
||||
_, values = calc_daily_return(df['Close'])
|
||||
features_dict['daily_return'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Multi-column indicators
|
||||
# Bollinger Bands
|
||||
print('Calculating multi-column indicator: bollinger')
|
||||
result = calc_bollinger(df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Stochastic Oscillator
|
||||
print('Calculating multi-column indicator: stochastic')
|
||||
result = calc_stochastic(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# SMA
|
||||
print('Calculating multi-column indicator: sma')
|
||||
result = calc_sma(df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# PSAR
|
||||
print('Calculating multi-column indicator: psar')
|
||||
result = calc_psar(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Donchian Channel
|
||||
print('Calculating multi-column indicator: donchian')
|
||||
result = calc_donchian(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Keltner Channel
|
||||
print('Calculating multi-column indicator: keltner')
|
||||
result = calc_keltner(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Ichimoku
|
||||
print('Calculating multi-column indicator: ichimoku')
|
||||
result = calc_ichimoku(df['High'], df['Low'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Elder Ray
|
||||
print('Calculating multi-column indicator: elder_ray')
|
||||
result = calc_elder_ray(df['Close'], df['Low'], df['High'])
|
||||
for subname, values in result:
|
||||
print(f"Adding subfeature: {subname}")
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
if os.path.exists(sub_feature_file):
|
||||
print(f'B Loading cached feature: {sub_feature_file}')
|
||||
arr = np.load(sub_feature_file)
|
||||
features_dict[subname] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Prepare lags, rolling stats, log returns, and volatility features sequentially
|
||||
# Lags
|
||||
for col in ohlcv_cols:
|
||||
for lag in range(1, lags + 1):
|
||||
feature_name = f'{col}_lag{lag}'
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'C Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing lag feature: {feature_name}')
|
||||
result = compute_lag(df, col, lag)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Rolling statistics
|
||||
for col in ohlcv_cols:
|
||||
for window in window_sizes:
|
||||
if (col == 'Open' and window == 5):
|
||||
continue
|
||||
if (col == 'High' and window == 5):
|
||||
continue
|
||||
if (col == 'High' and window == 30):
|
||||
continue
|
||||
if (col == 'Low' and window == 15):
|
||||
continue
|
||||
for stat in ['mean', 'std', 'min', 'max']:
|
||||
feature_name = f'{col}_roll_{stat}_{window}'
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'D Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing rolling stat feature: {feature_name}')
|
||||
result = compute_rolling(df, col, stat, window)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Log returns for different horizons
|
||||
for horizon in [5, 15, 30]:
|
||||
feature_name = f'log_return_{horizon}'
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'E Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing log return feature: {feature_name}')
|
||||
result = compute_log_return(df, horizon)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Volatility
|
||||
for window in window_sizes:
|
||||
feature_name = f'volatility_{window}'
|
||||
feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'F Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing volatility feature: {feature_name}')
|
||||
result = compute_volatility(df, window)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# --- Additional Technical Indicator Features ---
|
||||
# ADX
|
||||
adx_names = ['adx', 'adx_pos', 'adx_neg']
|
||||
adx_files = [f'./data/{csv_prefix}_{name}.npy' for name in adx_names]
|
||||
if all(os.path.exists(f) for f in adx_files):
|
||||
print('G Loading cached features: ADX')
|
||||
for name, f in zip(adx_names, adx_files):
|
||||
arr = np.load(f)
|
||||
features_dict[name] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating multi-column indicator: adx')
|
||||
result = calc_adx(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Force Index
|
||||
feature_file = f'./data/{csv_prefix}_force_index.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'K Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['force_index'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: force_index')
|
||||
_, values = calc_force_index(df['Close'], df['Volume'])
|
||||
features_dict['force_index'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Supertrend indicators
|
||||
for period, multiplier in [(12, 3.0), (10, 1.0), (11, 2.0)]:
|
||||
st_name = f'supertrend_{period}_{multiplier}'
|
||||
st_trend_name = f'supertrend_trend_{period}_{multiplier}'
|
||||
st_file = f'./data/{csv_prefix}_{st_name}.npy'
|
||||
st_trend_file = f'./data/{csv_prefix}_{st_trend_name}.npy'
|
||||
if os.path.exists(st_file) and os.path.exists(st_trend_file):
|
||||
print(f'L Loading cached features: {st_file}, {st_trend_file}')
|
||||
features_dict[st_name] = pd.Series(np.load(st_file), index=df.index)
|
||||
features_dict[st_trend_name] = pd.Series(np.load(st_trend_file), index=df.index)
|
||||
else:
|
||||
print(f'Calculating Supertrend indicator: {st_name}')
|
||||
st = ta.supertrend(df['High'], df['Low'], df['Close'], length=period, multiplier=multiplier)
|
||||
features_dict[st_name] = st[f'SUPERT_{period}_{multiplier}']
|
||||
features_dict[st_trend_name] = st[f'SUPERTd_{period}_{multiplier}']
|
||||
np.save(st_file, features_dict[st_name].values)
|
||||
np.save(st_trend_file, features_dict[st_trend_name].values)
|
||||
print(f'Saved features: {st_file}, {st_trend_file}')
|
||||
|
||||
# Concatenate all new features at once
|
||||
print('Concatenating all new features to DataFrame...')
|
||||
features_df = pd.DataFrame(features_dict)
|
||||
print("Columns in features_df:", features_df.columns.tolist())
|
||||
print("All-NaN columns in features_df:", features_df.columns[features_df.isna().all()].tolist())
|
||||
df = pd.concat([df, features_df], axis=1)
|
||||
|
||||
# Print all columns after concatenation
|
||||
print("All columns in df after concat:", df.columns.tolist())
|
||||
|
||||
# Downcast all float columns to save memory
|
||||
print('Downcasting float columns to save memory...')
|
||||
for col in df.columns:
|
||||
try:
|
||||
df[col] = pd.to_numeric(df[col], downcast='float')
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Add time features (exclude 'dayofweek')
|
||||
print('Adding hour feature...')
|
||||
df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
|
||||
df['hour'] = df['Timestamp'].dt.hour
|
||||
|
||||
# Handle NaNs after all feature engineering
|
||||
if IMPUTE_NANS:
|
||||
print('Imputing NaNs after feature engineering (using mean imputation)...')
|
||||
numeric_cols = df.select_dtypes(include=[np.number]).columns
|
||||
for col in numeric_cols:
|
||||
df[col] = df[col].fillna(df[col].mean())
|
||||
# If you want to impute non-numeric columns differently, add logic here
|
||||
else:
|
||||
print('Dropping NaNs after feature engineering...')
|
||||
df = df.dropna().reset_index(drop=True)
|
||||
|
||||
# Exclude 'Timestamp', 'Close', 'log_return', and any future target columns from features
|
||||
print('Selecting feature columns...')
|
||||
exclude_cols = ['Timestamp', 'Close', 'log_return', 'log_return_5', 'log_return_15', 'log_return_30']
|
||||
feature_cols = [col for col in df.columns if col not in exclude_cols]
|
||||
print('Features used for training:', feature_cols)
|
||||
|
||||
# Prepare CSV for results
|
||||
results_csv = './data/leave_one_out_results.csv'
|
||||
if not os.path.exists(results_csv):
|
||||
with open(results_csv, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['left_out_feature', 'used_features', 'rmse', 'mae', 'r2', 'mape', 'directional_accuracy'])
|
||||
|
||||
total_features = len(feature_cols)
|
||||
never_leave_out = {'Open', 'High', 'Low', 'Close', 'Volume'}
|
||||
for idx, left_out in enumerate(feature_cols):
|
||||
if left_out in never_leave_out:
|
||||
continue
|
||||
used = [f for f in feature_cols if f != left_out]
|
||||
print(f'\n=== Leave-one-out {idx+1}/{total_features}: left out {left_out} ===')
|
||||
try:
|
||||
# Prepare X and y for this combination
|
||||
X = df[used].values.astype(np.float32)
|
||||
y = df["log_return"].values.astype(np.float32)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
X_train, X_test = X[:split_idx], X[split_idx:]
|
||||
y_train, y_test = y[:split_idx], y[split_idx:]
|
||||
test_timestamps = df['Timestamp'].values[split_idx:]
|
||||
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
booster = model.train()
|
||||
model.save_model(f'./data/xgboost_model_wo_{left_out}.json')
|
||||
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
|
||||
# Reconstruct price series from log returns
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx]
|
||||
actual_prices = [start_price]
|
||||
for r_ in y_test:
|
||||
actual_prices.append(actual_prices[-1] * np.exp(r_))
|
||||
actual_prices = np.array(actual_prices[1:])
|
||||
predicted_prices = [start_price]
|
||||
for r_ in test_preds:
|
||||
predicted_prices.append(predicted_prices[-1] * np.exp(r_))
|
||||
predicted_prices = np.array(predicted_prices[1:])
|
||||
|
||||
mae = mean_absolute_error(actual_prices, predicted_prices)
|
||||
r2 = r2_score(actual_prices, predicted_prices)
|
||||
direction_actual = np.sign(np.diff(actual_prices))
|
||||
direction_pred = np.sign(np.diff(predicted_prices))
|
||||
directional_accuracy = (direction_actual == direction_pred).mean()
|
||||
mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
|
||||
|
||||
# Save results to CSV
|
||||
with open(results_csv, 'a', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow([left_out, "|".join(used), rmse, mae, r2, mape, directional_accuracy])
|
||||
print(f'Left out {left_out}: RMSE={rmse:.4f}, MAE={mae:.4f}, R2={r2:.4f}, MAPE={mape:.2f}%, DirAcc={directional_accuracy*100:.2f}%')
|
||||
|
||||
# Plotting for this run
|
||||
plot_prefix = f'loo_{left_out}'
|
||||
print('Plotting distribution of absolute prediction errors...')
|
||||
plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
|
||||
print('Plotting directional accuracy...')
|
||||
plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=plot_prefix)
|
||||
except Exception as e:
|
||||
print(f'Leave-one-out failed for {left_out}: {e}')
|
||||
print(f'All leave-one-out runs completed. Results saved to {results_csv}')
|
||||
sys.exit(0)
|
||||
318
xgboost/plot_results.py
Normal file
318
xgboost/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)
|
||||
|
||||
Reference in New Issue
Block a user