SpikeMethod for CSV data

SpikeMethod.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress

file_name = "Pydata1.csv"
data = pd.read_csv(file_name, sep=";", parse_dates=["Time"], index_col="Time")

data.columns = data.columns.str.strip().str.lower()
data = data.dropna()

print("Kolumnnamn i datan:", data.columns)
print("Första raderna i datan:\n", data.head())

def calculate_bollinger_bands(df, window=20, num_std=2):
    df['sma'] = df['price'].rolling(window=window).mean()
    df['std'] = df['price'].rolling(window=window).std()
    df['upper band'] = df['sma'] + (num_std * df['std'])
    df['lower band'] = df['sma'] - (num_std * df['std'])
    return df

data = calculate_bollinger_bands(data)

interval = '120T'
bar_times = data.resample(interval).first().dropna()

if bar_times.empty:
    bar_times = data.resample(interval).first().ffill()
if bar_times.empty:
    raise ValueError("Resampling skapade en tom dataframe! Kontrollera din data.")

bar_prices = bar_times['price'].dropna()
bar_prices.index = range(len(bar_prices))

slope, intercept, _, _, _ = linregress(bar_prices.index, bar_prices.values)
trend_line = intercept + slope * np.array(bar_prices.index)

matching_bollinger = data.reindex(bar_times.index).dropna()

min_length = min(len(matching_bollinger.index), len(bar_prices))

bar_prices = bar_prices.iloc[:min_length]
matching_bollinger = matching_bollinger.iloc[:min_length]
trend_line = trend_line[:min_length]

breakout = any(
    (trend_line > matching_bollinger['upper band']).values |
    (trend_line < matching_bollinger['lower band']).values
)

print("Längd på matching_bollinger.index:", len(matching_bollinger.index))
print("Längd på bar_prices:", len(bar_prices))
print(f"Breakout detected: {breakout}")

plt.figure(figsize=(12, 6))
plt.plot(data.index, data['price'], label='Price', color='blue', alpha=0.5)
plt.plot(data.index, data['upper band'], label='Upper BB', linestyle='dashed', color='red')
plt.plot(data.index, data['lower band'], label='Lower BB', linestyle='dashed', color='red')
plt.scatter(matching_bollinger.index, bar_prices, color='black', label='Two-hour Bars', zorder=3)
plt.plot(matching_bollinger.index, trend_line, label='Trend Line', color='green', linewidth=2)
plt.legend()
plt.title(f'Breakout Detected: {breakout}')
plt.xlabel('Time')
plt.ylabel('Price')
plt.xticks(rotation=45)
plt.show()