260 lines
11 KiB
Python
260 lines
11 KiB
Python
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import pandas as pd
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import numpy as np
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import ta
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import logging
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import mplfinance as mpf
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from matplotlib.patches import Rectangle
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class TrendDetectorMACD:
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def __init__(self, data, verbose=False):
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self.data = data
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self.verbose = verbose
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# Configure logging
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logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
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format='%(asctime)s - %(levelname)s - %(message)s')
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self.logger = logging.getLogger('TrendDetector')
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# Convert data to pandas DataFrame if it's not already
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if not isinstance(self.data, pd.DataFrame):
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if isinstance(self.data, list):
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self.logger.info("Converting list to DataFrame")
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self.data = pd.DataFrame({'close': self.data})
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else:
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self.logger.error("Invalid data format provided")
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raise ValueError("Data must be a pandas DataFrame or a list")
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self.logger.info(f"Initialized TrendDetector with {len(self.data)} data points")
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def detect_trends_MACD_signal(self):
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self.logger.info("Starting trend detection")
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if len(self.data) < 3:
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self.logger.warning("Not enough data points for trend detection")
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return {"error": "Not enough data points for trend detection"}
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# Create a copy of the DataFrame to avoid modifying the original
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df = self.data.copy()
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self.logger.info("Created copy of input data")
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# If 'close' column doesn't exist, try to use a relevant column
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if 'close' not in df.columns and len(df.columns) > 0:
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self.logger.info(f"'close' column not found, using {df.columns[0]} instead")
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df['close'] = df[df.columns[0]] # Use the first column as 'close'
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# Add trend indicators
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self.logger.info("Calculating MACD indicators")
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# Moving Average Convergence Divergence (MACD)
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df['macd'] = ta.trend.macd(df['close'])
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df['macd_signal'] = ta.trend.macd_signal(df['close'])
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df['macd_diff'] = ta.trend.macd_diff(df['close'])
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# Directional Movement Index (DMI)
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if all(col in df.columns for col in ['high', 'low', 'close']):
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self.logger.info("Calculating ADX indicators")
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df['adx'] = ta.trend.adx(df['high'], df['low'], df['close'])
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df['adx_pos'] = ta.trend.adx_pos(df['high'], df['low'], df['close'])
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df['adx_neg'] = ta.trend.adx_neg(df['high'], df['low'], df['close'])
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# Identify trend changes
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self.logger.info("Identifying trend changes")
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df['trend'] = np.where(df['macd'] > df['macd_signal'], 'up', 'down')
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df['trend_change'] = df['trend'] != df['trend'].shift(1)
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# Generate trend segments
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self.logger.info("Generating trend segments")
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trends = []
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trend_start = 0
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for i in range(1, len(df)):
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if df['trend_change'].iloc[i]:
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if i > trend_start:
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trends.append({
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"type": df['trend'].iloc[i-1],
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"start_index": trend_start,
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"end_index": i-1,
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"start_value": df['close'].iloc[trend_start],
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"end_value": df['close'].iloc[i-1]
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})
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trend_start = i
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# Add the last trend
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if trend_start < len(df):
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trends.append({
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"type": df['trend'].iloc[-1],
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"start_index": trend_start,
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"end_index": len(df)-1,
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"start_value": df['close'].iloc[trend_start],
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"end_value": df['close'].iloc[-1]
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})
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self.logger.info(f"Detected {len(trends)} trend segments")
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return trends
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def get_strongest_trend(self):
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self.logger.info("Finding strongest trend")
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trends = self.detect_trends_MACD_signal()
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if isinstance(trends, dict) and "error" in trends:
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self.logger.warning(f"Error in trend detection: {trends['error']}")
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return trends
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if not trends:
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self.logger.info("No significant trends detected")
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return {"message": "No significant trends detected"}
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strongest = max(trends, key=lambda x: abs(x["end_value"] - x["start_value"]))
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self.logger.info(f"Strongest trend: {strongest['type']} from index {strongest['start_index']} to {strongest['end_index']}")
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return strongest
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def plot_trends(self, trends):
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"""
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Plot price data with identified trends highlighted using candlestick charts.
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"""
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self.logger.info("Plotting trends with candlesticks")
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if isinstance(trends, dict) and "error" in trends:
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self.logger.error(trends["error"])
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print(trends["error"])
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return
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if not trends:
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self.logger.warning("No significant trends detected for plotting")
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print("No significant trends detected")
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return
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# Create a figure with 2 subplots that share the x-axis
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={'height_ratios': [2, 1]}, sharex=True)
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self.logger.info("Creating plot figure with shared x-axis")
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# Prepare data for candlestick chart
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df = self.data.copy()
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# Ensure required columns exist for candlestick
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required_cols = ['open', 'high', 'low', 'close']
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if not all(col in df.columns for col in required_cols):
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self.logger.warning("Missing required columns for candlestick. Defaulting to line chart.")
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if 'close' in df.columns:
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ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'],
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df['close'], color='black', alpha=0.7, linewidth=1, label='Price')
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else:
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ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'],
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df[df.columns[0]], color='black', alpha=0.7, linewidth=1, label='Price')
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else:
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# Get x values (dates if available, otherwise indices)
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if 'datetime' in df.columns:
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x_label = 'Date'
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# Format date axis
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ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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fig.autofmt_xdate()
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self.logger.info("Using datetime for x-axis")
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# For candlestick, ensure datetime is the index
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if df.index.name != 'datetime':
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df = df.set_index('datetime')
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else:
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x_label = 'Index'
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self.logger.info("Using index for x-axis")
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# Plot candlestick chart
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up_color = 'green'
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down_color = 'red'
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# Draw candlesticks manually
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width = 0.6
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for i in range(len(df)):
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# Get OHLC values for this candle
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open_val = df['open'].iloc[i]
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close_val = df['close'].iloc[i]
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high_val = df['high'].iloc[i]
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low_val = df['low'].iloc[i]
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idx = df.index[i]
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# Determine candle color
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color = up_color if close_val >= open_val else down_color
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# Plot candle body
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body_height = abs(close_val - open_val)
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bottom = min(open_val, close_val)
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rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
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ax1.add_patch(rect)
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# Plot candle wicks
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ax1.plot([i, i], [low_val, high_val], color='black', linewidth=1)
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# Set appropriate x-axis limits
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ax1.set_xlim(-0.5, len(df) - 0.5)
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# Highlight each trend with a different color
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self.logger.info("Highlighting trends on plot")
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for trend in trends:
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start_idx = trend['start_index']
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end_idx = trend['end_index']
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trend_type = trend['type']
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# Get x-coordinates for trend plotting
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x_start = start_idx
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x_end = end_idx
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# Get y-coordinates for trend line
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if 'close' in df.columns:
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y_start = df['close'].iloc[start_idx]
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y_end = df['close'].iloc[end_idx]
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else:
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y_start = df[df.columns[0]].iloc[start_idx]
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y_end = df[df.columns[0]].iloc[end_idx]
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# Choose color based on trend type
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color = 'green' if trend_type == 'up' else 'red'
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# Plot trend line
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ax1.plot([x_start, x_end], [y_start, y_end], color=color, linewidth=2,
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label=f"{trend_type.capitalize()} Trend" if f"{trend_type.capitalize()} Trend" not in ax1.get_legend_handles_labels()[1] else "")
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# Add markers at start and end points
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ax1.scatter(x_start, y_start, color=color, marker='o', s=50)
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ax1.scatter(x_end, y_end, color=color, marker='s', s=50)
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# Configure first subplot
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ax1.set_title('Price with Trends (Candlestick)', fontsize=16)
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ax1.set_ylabel('Price', fontsize=14)
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ax1.grid(alpha=0.3)
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ax1.legend()
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# Create MACD in second subplot
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self.logger.info("Creating MACD subplot")
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# Calculate MACD indicators if not already present
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if 'macd' not in df.columns:
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if 'close' not in df.columns and len(df.columns) > 0:
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df['close'] = df[df.columns[0]]
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df['macd'] = ta.trend.macd(df['close'])
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df['macd_signal'] = ta.trend.macd_signal(df['close'])
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df['macd_diff'] = ta.trend.macd_diff(df['close'])
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# Plot MACD components on second subplot
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x_indices = np.arange(len(df))
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ax2.plot(x_indices, df['macd'], label='MACD', color='blue')
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ax2.plot(x_indices, df['macd_signal'], label='Signal', color='orange')
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# Plot MACD histogram
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for i in range(len(df)):
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if df['macd_diff'].iloc[i] >= 0:
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ax2.bar(i, df['macd_diff'].iloc[i], color='green', alpha=0.5, width=0.8)
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else:
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ax2.bar(i, df['macd_diff'].iloc[i], color='red', alpha=0.5, width=0.8)
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ax2.set_title('MACD Indicator', fontsize=16)
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ax2.set_xlabel(x_label, fontsize=14)
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ax2.set_ylabel('MACD', fontsize=14)
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ax2.grid(alpha=0.3)
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ax2.legend()
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# Enable synchronized zooming
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plt.tight_layout()
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plt.subplots_adjust(hspace=0.1)
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plt.show()
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return plt
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