FilesExpand file tree

main

/

utils.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

113 lines (106 loc) · 5.19 KB

main

/

utils.py

Copy path

Top

File metadata and controls

• Code
• Blame

113 lines (106 loc) · 5.19 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

# utils.py

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

import pandas as pd

from datetime import datetime, timedelta

import warnings

warnings.filterwarnings('ignore')

plt.style.use('seaborn-v0_8')

import torch

def plot_results(dates, actuals, preds, title, config):

plt.style.use('dark_background')

plt.figure(figsize=(12, 6), facecolor='#1c2526')

plt.plot(dates, actuals, label='Actual', color='#00ff00', linewidth=2)

plt.plot(dates, preds, '--', label='Predicted', color='#00f7ff', linewidth=2)

plt.title(f"{title} - {config.ticker}", fontsize=14, color='white')

plt.xlabel('Date', fontsize=12, color='white')

plt.ylabel('Price (USD)', fontsize=12, color='white')

plt.legend(fontsize=10, facecolor='#1c2526', edgecolor='white', labelcolor='white')

plt.grid(True, alpha=0.3, color='#cccccc')

plt.gcf().autofmt_xdate()

plt.tick_params(colors='white')

plt.tight_layout()

plt.show()

def plot_forecast(dates, prices, forecast_dates, forecast_prices, std, config):

plt.style.use('dark_background')

plt.figure(figsize=(14, 8), facecolor='#1c2526')

past_days = config.past_days_plot

historical_dates = dates[-past_days:]

historical_prices = prices[-past_days:]

plt.plot(historical_dates, historical_prices, 'o-', label='Historical (Past 60 Days)', color='#ff00ff', linewidth=2)

plt.plot(forecast_dates, forecast_prices, 'o-', label=f'Forecast (Next {config.forecast_days} Days)', color='#ffff00', linewidth=2, markersize=6)

plt.fill_between(forecast_dates, forecast_prices - std, forecast_prices + std, alpha=0.2, color='#ffff00', label='Confidence Interval')

plt.axvline(x=dates[-1], color='#cccccc', linestyle='--', alpha=0.7, label='Today')

plt.title(f"{config.ticker} - Past {past_days} Days & {config.forecast_days}-Day Forecast", fontsize=14, color='white')

plt.xlabel('Date', fontsize=12, color='white')

plt.ylabel('Price (USD)', fontsize=12, color='white')

plt.legend(fontsize=10, facecolor='#1c2526', edgecolor='white', labelcolor='white')

plt.grid(True, alpha=0.3, color='#cccccc')

plt.gcf().autofmt_xdate()

plt.tick_params(colors='white')

plt.tight_layout()

print(f"\n{config.ticker} - {config.forecast_days} Day Forecast:")

print("-" * 50)

for date, price in zip(forecast_dates, forecast_prices):

print(f"{date.strftime('%Y-%m-%d (%A)')}: ${price:.2f}")

plt.show()

def predict_future(model, last_window, num_days, data_handler, config):

model.eval()

predictions_scaled = []

current_window = last_window.copy()

for _ in range(num_days):

input_tensor = torch.FloatTensor(current_window).unsqueeze(0).to(config.device)

with torch.no_grad():

pred_scaled = model(input_tensor).cpu().item()

predictions_scaled.append(pred_scaled)

current_window = np.roll(current_window, -1, axis=0)

current_window[-1, 0] = pred_scaled

return data_handler.inverse_target_transform(np.array(predictions_scaled))

def plot_frequency_and_heatmap(data: pd.DataFrame, ticker: str, features: list, technical_indicators: list):

plt.style.use('dark_background')

all_features = list(dict.fromkeys(features + technical_indicators))

available_features = [f for f in all_features if f in data.columns]

hist_features = ['Close', 'RSI', 'SMA_10', 'BB_Upper', 'BB_Lower', 'Volatility_10', 'Volatility_20', 'Price_Change', 'MACD']

hist_features = [f for f in hist_features if f in available_features]

if not hist_features:

print("Warning: No valid features for histogram. Skipping frequency plot.")

return

plt.figure(figsize=(15, 10), facecolor='#1c2526')

colors = ['#00ffab', '#ff6f61', '#ffd700', '#6ab04c', '#ff85ff', '#00b7eb', '#ff9f43', '#5c5c8a', '#ff4f81']

for i, feature in enumerate(hist_features, 1):

plt.subplot(3, 3, i)

data_clean = data[feature].replace([np.inf, -np.inf], np.nan).dropna()

if data_clean.empty:

print(f"Warning: No valid data for {feature}. Skipping histogram.")

continue

sns.histplot(data_clean, bins=20, kde=True, color=colors[i-1], edgecolor='white', alpha=0.7)

plt.title(f'{feature} Distribution', fontsize=10, color='white')

plt.xlabel(feature, fontsize=8, color='white')

plt.ylabel('Frequency', fontsize=8, color='white')

plt.grid(True, alpha=0.3, color='gray')

plt.tick_params(colors='white')

plt.suptitle(f'{ticker} Feature Distributions', fontsize=14, color='white')

plt.tight_layout(rect=[0, 0, 1, 0.95])

plt.show()

plt.figure(figsize=(12, 10), facecolor='#1c2526')

correlation_matrix = data[available_features].corr()

sns.heatmap(

correlation_matrix,

annot=True,

cmap='Spectral',

center=0,

vmin=-1,

vmax=1,

fmt='.2f',

square=True,

cbar_kws={'label': 'Correlation', 'shrink': 0.8},

annot_kws={'size': 8, 'color': 'white'},

linewidths=0.5,

linecolor='gray'

)

plt.title(f'{ticker} Feature Correlation Heatmap', fontsize=14, color='white')

plt.xticks(rotation=45, ha='right', color='white')

plt.yticks(rotation=0, color='white')

plt.tight_layout()

plt.show()