168 lines
6.7 KiB
Python
168 lines
6.7 KiB
Python
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import numpy as np
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import pandas as pd
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from sqlalchemy import create_engine
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from scipy.signal import find_peaks
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import matplotlib.pyplot as plt
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import matplotlib
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from sklearn.linear_model import LinearRegression
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from matplotlib.widgets import Slider
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class BitcoinTrendAnalysis:
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def __init__(self, db_path):
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self.df = None
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self.db_path = db_path
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self.engine = create_engine(f'sqlite:///{self.db_path}')
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def load_data(self):
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self.df = pd.read_sql(
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"SELECT Timestamp, Close FROM bitcoin_data WHERE strftime('%Y', Timestamp) >= '2019'",
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self.engine,
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index_col='Timestamp',
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parse_dates=['Timestamp']
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)
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if self.df is not None and not self.df.empty:
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print(f"Data loaded successfully. Shape: {self.df.shape}")
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else:
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print("Failed to load data. DataFrame is empty or None.")
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def adaptive_find_peaks(self, smooth_prices, window, factor, distance):
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print(factor)
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prominences = np.zeros_like(smooth_prices)
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for i in range(len(smooth_prices)):
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start = max(0, i - window // 2)
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end = min(len(smooth_prices), i + window // 2)
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local_max = np.max(smooth_prices[start:end])
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local_min = np.min(smooth_prices[start:end])
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prominences[i] = (local_max - local_min) * factor
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print(prominences)
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peaks, _ = find_peaks(smooth_prices, prominence=prominences, distance=distance)
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valleys, _ = find_peaks(-smooth_prices, prominence=prominences, distance=distance)
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return peaks, valleys, prominences
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def analyze_trends_peaks(self, resample_window='D', smoothing_window=10, prominence_factor=0.5, window=30,
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distance=None):
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matplotlib.use('TkAgg')
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if not hasattr(self, 'df') or self.df is None:
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print("Data not loaded. Call load_and_prepare_data() first.")
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return
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self.df = self.df.resample(resample_window).agg({'Close': 'last'})
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prices = self.df['Close'].values
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smooth_prices = pd.Series(prices).rolling(window=smoothing_window).mean()
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fig, ax = plt.subplots(figsize=(14, 7))
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plt.subplots_adjust(bottom=0.25) # Space for widgets
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ax2 = ax.twinx() # Secondary axis for prominence
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# Initial peaks and prominences
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peaks, valleys, prominences = self.adaptive_find_peaks(smooth_prices, window=window, factor=prominence_factor,
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distance=distance)
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# Plot main price curve
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price_line, = ax.plot(self.df.index, smooth_prices, label='Bitcoin Smooth Price')
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# Scatter plots for peaks/valleys
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peaks_plot = ax.scatter(self.df.index[peaks], smooth_prices[peaks], color='green', s=100, marker='^',
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label='Local Maxima')
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valleys_plot = ax.scatter(self.df.index[valleys], smooth_prices[valleys], color='red', s=100, marker='v',
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label='Local Minima')
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# Prominence line on secondary y-axis
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prominence_line, = ax2.plot(self.df.index, prominences, color="purple", linestyle="dashed", alpha=0.7,
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label="Prominence")
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ax2.set_ylabel("Prominence")
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ax.set_title(f'Bitcoin Price Trends Analysis\nfactor={prominence_factor}')
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ax.set_xlabel('Date')
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ax.set_ylabel('Price')
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ax.legend()
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ax2.legend(loc="upper right")
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ax.grid(True)
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# Slider setup
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ax_slider = plt.axes([0.2, 0.05, 0.65, 0.03]) # Positioning of slider
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slider = Slider(ax_slider, 'Prom Factor', 0.1, 2.0, valinit=prominence_factor, valstep=0.05)
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# Update function for slider
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def update_plot(factor):
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# Recalculate peaks and prominences
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peaks, valleys, prominences = self.adaptive_find_peaks(smooth_prices.to_numpy(), window=window,
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factor=factor, distance=distance)
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print(len(peaks))
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# Update scatter points for peaks
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peaks_plot.set_offsets(np.column_stack([
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(self.df.index[peaks] - np.datetime64('1970-01-01')) / np.timedelta64(1, 's'),
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smooth_prices[peaks]
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]))
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# Update scatter points for valleys
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valleys_plot.set_offsets(np.column_stack([
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(self.df.index[valleys] - np.datetime64('1970-01-01')) / np.timedelta64(1, 's'),
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smooth_prices[valleys]
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]))
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# Update prominence line
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prominence_line.set_ydata(prominences)
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# Update the title to reflect the current prominence factor
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ax.set_title(f'Bitcoin Price Trends Analysis\nfactor={factor}')
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# Redraw the figure
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fig.canvas.draw_idle()
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slider.on_changed(update_plot) # Update plot when slider changes
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plt.show()
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def analyze_trends_linear_regression(self):
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if self.df is None or self.df.empty:
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print("No data loaded.")
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return
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self.df['Timestamp_num'] = (self.df.index - self.df.index[0]).total_seconds()
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x = self.df['Timestamp_num'].values.reshape(-1, 1)
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y = self.df['Close'].values
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model = LinearRegression()
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model.fit(x, y)
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trend_line = model.predict(x)
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matplotlib.use('TkAgg')
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fig, ax = plt.subplots(figsize=(14, 7))
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plt.subplots_adjust(bottom=0.2)
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ax.plot(self.df.index, self.df['Close'], label='Bitcoin Price', color='blue')
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ax.plot(self.df.index, trend_line, label='Linear Trend', color='red', linestyle='dashed')
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ax.set_title("Bitcoin Price Linear Trend")
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ax.set_xlabel("Date")
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ax.set_ylabel("Price")
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ax.legend()
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ax.grid(True)
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def zoom(event):
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scale_factor = 1.2 if event.button == 'up' else 0.8
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xlim = ax.get_xlim()
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x_range = (xlim[1] - xlim[0]) * scale_factor
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x_mid = (xlim[0] + xlim[1]) / 2
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ax.set_xlim(x_mid - x_range / 2, x_mid + x_range / 2)
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ax.figure.canvas.draw()
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def pan(event):
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step = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.1
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if event.key == 'right':
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ax.set_xlim(ax.get_xlim()[0] + step, ax.get_xlim()[1] + step)
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elif event.key == 'left':
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ax.set_xlim(ax.get_xlim()[0] - step, ax.get_xlim()[1] - step)
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ax.figure.canvas.draw()
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fig.canvas.mpl_connect('scroll_event', zoom)
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fig.canvas.mpl_connect('key_press_event', pan)
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plt.show()
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slope = model.coef_[0]
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print(f"Trend Slope: {slope:.6f} (positive = uptrend, negative = downtrend)")
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