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Refactor cycle detection and trend analysis; enhance trend detection with linear regression and moving averages. Update main script for improved data handling and visualization.

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This commit is contained in:
Simon Moisy 2025-05-09 12:23:45 +08:00
parent cbc6a7493d
commit e9bfcd03eb
4 changed files with 762 additions and 711 deletions
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1
main.py
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@ -6,6 +6,7 @@ from cycle_detector import CycleDetector
# Load data from CSV file instead of database
data = pd.read_csv('data/btcusd_1-day_data.csv')
# Convert datetime column to datetime type
start_date = pd.to_datetime('2025-04-01')
stop_date = pd.to_datetime('2025-05-06')

102
trend_detector_simple.py
View File

@ -4,6 +4,7 @@ import logging
from scipy.signal import find_peaks
import matplotlib.dates as mdates
from scipy import stats
from scipy import stats
class TrendDetectorSimple:
def __init__(self, data, verbose=False):
@ -18,6 +19,44 @@ class TrendDetectorSimple:
self.data = data
self.verbose = verbose
# Plot style configuration
self.plot_style = 'dark_background'
self.bg_color = '#181C27'
self.plot_size = (12, 8)
# Candlestick configuration
self.candle_width = 0.6
self.candle_up_color = '#089981'
self.candle_down_color = '#F23645'
self.candle_alpha = 0.8
self.wick_width = 1
# Marker configuration
self.min_marker = '^'
self.min_color = 'red'
self.min_size = 100
self.max_marker = 'v'
self.max_color = 'green'
self.max_size = 100
self.marker_zorder = 100
# Line configuration
self.line_width = 2
self.min_line_style = 'g--' # green dashed
self.max_line_style = 'r--' # red dashed
self.sma7_line_style = 'y-' # yellow solid
self.sma15_line_style = 'm-' # magenta solid
# Text configuration
self.title_size = 14
self.title_color = 'white'
self.axis_label_size = 12
self.axis_label_color = 'white'
# Legend configuration
self.legend_loc = 'best'
self.legend_bg_color = '#333333'
# Configure logging
logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
format='%(asctime)s - %(levelname)s - %(message)s')
@ -34,6 +73,7 @@ class TrendDetectorSimple:
self.logger.info(f"Initialized TrendDetectorSimple with {len(self.data)} data points")
def detect_trends(self):
def detect_trends(self):
"""
Detect trends by identifying local minima and maxima in the price data
@ -47,10 +87,13 @@ class TrendDetectorSimple:
- DataFrame with columns for timestamps, prices, and trend indicators
"""
self.logger.info(f"Detecting trends")
self.logger.info(f"Detecting trends")
df = self.data.copy()
close_prices = df['close'].values
max_peaks, _ = find_peaks(close_prices)
min_peaks, _ = find_peaks(-close_prices)
max_peaks, _ = find_peaks(close_prices)
min_peaks, _ = find_peaks(-close_prices)
@ -79,11 +122,9 @@ class TrendDetectorSimple:
# Calculate Simple Moving Averages (SMA) for 7 and 15 periods
self.logger.info("Calculating SMA-7 and SMA-15")
# Calculate SMA values and exclude NaN values
sma_7 = df['close'].rolling(window=7).mean().dropna().values
sma_15 = df['close'].rolling(window=15).mean().dropna().values
sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
# Add SMA values to regression_results
analysis_results = {}
analysis_results['linear_regression'] = {
'min': {
@ -107,6 +148,7 @@ class TrendDetectorSimple:
return result, analysis_results
def plot_trends(self, trend_data, analysis_results):
def plot_trends(self, trend_data, analysis_results):
"""
Plot the price data with detected trends using a candlestick chart.
@ -120,18 +162,18 @@ class TrendDetectorSimple:
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
# Create the figure and axis
fig, ax = plt.subplots(figsize=(12, 8))
# Create the figure and axis with specified background
plt.style.use(self.plot_style)
fig, ax = plt.subplots(figsize=self.plot_size)
# Set the custom background color
fig.patch.set_facecolor(self.bg_color)
ax.set_facecolor(self.bg_color)
# Create a copy of the data
df = self.data.copy()
# Plot candlestick chart
up_color = 'green'
down_color = 'red'
# Draw candlesticks manually
width = 0.6
x_values = range(len(df))
for i in range(len(df)):
@ -142,26 +184,29 @@ class TrendDetectorSimple:
low_val = df['low'].iloc[i]
# Determine candle color
color = up_color if close_val >= open_val else down_color
color = self.candle_up_color if close_val >= open_val else self.candle_down_color
# Plot candle body
body_height = abs(close_val - open_val)
bottom = min(open_val, close_val)
rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height,
color=color, alpha=self.candle_alpha)
ax.add_patch(rect)
# Plot candle wicks
ax.plot([i, i], [low_val, high_val], color='black', linewidth=1)
ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
if min_indices:
min_y = [df['close'].iloc[i] for i in min_indices]
ax.scatter(min_indices, min_y, color='darkred', s=200, marker='^', label='Local Minima', zorder=100)
ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size,
marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
if max_indices:
max_y = [df['close'].iloc[i] for i in max_indices]
ax.scatter(max_indices, max_y, color='darkgreen', s=200, marker='v', label='Local Maxima', zorder=100)
ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size,
marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
if analysis_results:
x_vals = np.arange(len(df))
@ -169,33 +214,38 @@ class TrendDetectorSimple:
min_slope = analysis_results['linear_regression']['min']['slope']
min_intercept = analysis_results['linear_regression']['min']['intercept']
min_line = min_slope * x_vals + min_intercept
ax.plot(x_vals, min_line, 'g--', linewidth=2, label='Minima Regression')
ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width,
label='Minima Regression')
# Maxima regression line (resistance)
max_slope = analysis_results['linear_regression']['max']['slope']
max_intercept = analysis_results['linear_regression']['max']['intercept']
max_line = max_slope * x_vals + max_intercept
ax.plot(x_vals, max_line, 'r--', linewidth=2, label='Maxima Regression')
ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width,
label='Maxima Regression')
# SMA-7 line
sma_7 = analysis_results['sma']['7']
ax.plot(x_vals, sma_7, 'y-', linewidth=2, label='SMA-7')
ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width,
label='SMA-7')
# SMA-15 line
# sma_15 = analysis_results['sma']['15']
# valid_idx_15 = ~np.isnan(sma_15)
# ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], 'm-', linewidth=2, label='SMA-15')
sma_15 = analysis_results['sma']['15']
valid_idx_15 = ~np.isnan(sma_15)
ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style,
linewidth=self.line_width, label='SMA-15')
# Set title and labels
ax.set_title('Price Candlestick Chart with Local Minima and Maxima', fontsize=14)
ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Price', fontsize=12)
ax.set_title('Price Candlestick Chart with Local Minima and Maxima',
fontsize=self.title_size, color=self.title_color)
ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
# Set appropriate x-axis limits
ax.set_xlim(-0.5, len(df) - 0.5)
# Add a legend
ax.legend(loc='best')
ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
# Adjust layout
plt.tight_layout()