Cleanup
This commit is contained in:
Simon Moisy
parent
e4ded694b1
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44
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# Use the base image with CUDA and PyTorch
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FROM kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1:1.1.1
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# Install NVIDIA Container Toolkit (necessary for GPU support)
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RUN apt-get update && apt-get install -y \
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nvidia-container-runtime \
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python3 \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# Install necessary dependencies and configure NVIDIA repository
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RUN apt-get update && apt-get install -y \
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curl \
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gnupg \
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lsb-release \
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sudo \
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&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
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&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
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sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
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tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
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&& sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list \
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&& apt-get update
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# Install NVIDIA Container Toolkit
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RUN apt-get install -y nvidia-container-toolkit
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# Set the environment variables for CUDA
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ENV PATH=/usr/local/cuda-11.7/bin:$PATH
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ENV LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
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# Set the runtime for GPU (requires NVIDIA runtime to be installed on the host machine)
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ENV NVIDIA_VISIBLE_DEVICES=all
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# Set working directory to /projects
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WORKDIR /project
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# Install necessary Python dependencies
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# Uncomment and modify the next lines as per your project requirements
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COPY requirements.txt requirements.txt
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RUN pip3 install -r requirements.txt
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# Run your Python script
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CMD ["python3", "main.py"]
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.signal import argrelextrema
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class CycleDetector:
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def __init__(self, data, timeframe='daily'):
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"""
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Initialize the CycleDetector with price data.
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Parameters:
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- data: DataFrame with at least 'date' or 'datetime' and 'close' columns
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- timeframe: 'daily', 'weekly', or 'monthly'
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"""
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self.data = data.copy()
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self.timeframe = timeframe
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# Ensure we have a consistent date column name
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if 'datetime' in self.data.columns and 'date' not in self.data.columns:
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self.data.rename(columns={'datetime': 'date'}, inplace=True)
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# Convert data to specified timeframe if needed
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if timeframe == 'weekly' and 'date' in self.data.columns:
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self.data = self._convert_data(self.data, 'W')
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elif timeframe == 'monthly' and 'date' in self.data.columns:
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self.data = self._convert_data(self.data, 'M')
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# Add columns for local minima and maxima detection
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self._add_swing_points()
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def _convert_data(self, data, timeframe):
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"""Convert daily data to 'timeframe' timeframe."""
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data['date'] = pd.to_datetime(data['date'])
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data.set_index('date', inplace=True)
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weekly = data.resample(timeframe).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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})
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return weekly.reset_index()
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def _add_swing_points(self, window=5):
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"""
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Identify swing points (local minima and maxima).
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Parameters:
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- window: The window size for local minima/maxima detection
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"""
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# Set the index to make calculations easier
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if 'date' in self.data.columns:
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self.data.set_index('date', inplace=True)
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# Detect local minima (swing lows)
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min_idx = argrelextrema(self.data['low'].values, np.less, order=window)[0]
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self.data['swing_low'] = False
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self.data.iloc[min_idx, self.data.columns.get_loc('swing_low')] = True
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# Detect local maxima (swing highs)
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max_idx = argrelextrema(self.data['high'].values, np.greater, order=window)[0]
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self.data['swing_high'] = False
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self.data.iloc[max_idx, self.data.columns.get_loc('swing_high')] = True
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# Reset index
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self.data.reset_index(inplace=True)
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def find_cycle_lows(self):
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"""Find all swing lows which represent cycle lows."""
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swing_low_dates = self.data[self.data['swing_low']]['date'].values
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return swing_low_dates
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def calculate_cycle_lengths(self):
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"""Calculate the lengths of each cycle between consecutive lows."""
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swing_low_indices = np.where(self.data['swing_low'])[0]
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cycle_lengths = np.diff(swing_low_indices)
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return cycle_lengths
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def get_average_cycle_length(self):
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"""Calculate the average cycle length."""
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cycle_lengths = self.calculate_cycle_lengths()
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if len(cycle_lengths) > 0:
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return np.mean(cycle_lengths)
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return None
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def get_cycle_window(self, tolerance=0.10):
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"""
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Get the cycle window with the specified tolerance.
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Parameters:
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- tolerance: The tolerance as a percentage (default: 10%)
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Returns:
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- tuple: (min_cycle_length, avg_cycle_length, max_cycle_length)
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"""
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avg_length = self.get_average_cycle_length()
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if avg_length is not None:
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min_length = avg_length * (1 - tolerance)
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max_length = avg_length * (1 + tolerance)
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return (min_length, avg_length, max_length)
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return None
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def detect_two_drives_pattern(self, lookback=10):
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"""
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Detect 2-drives pattern: a swing low, counter trend bounce, and a lower low.
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Parameters:
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- lookback: Number of periods to look back
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Returns:
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- list: Indices where 2-drives patterns are detected
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"""
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patterns = []
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for i in range(lookback, len(self.data) - 1):
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if not self.data.iloc[i]['swing_low']:
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continue
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# Get the segment of data to check for pattern
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segment = self.data.iloc[i-lookback:i+1]
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swing_lows = segment[segment['swing_low']]['low'].values
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if len(swing_lows) >= 2 and swing_lows[-1] < swing_lows[-2]:
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# Check if there was a bounce between the two lows
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between_lows = segment.iloc[-len(swing_lows):-1]
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if len(between_lows) > 0 and max(between_lows['high']) > swing_lows[-2]:
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patterns.append(i)
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return patterns
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def detect_v_shaped_lows(self, window=5, threshold=0.02):
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"""
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Detect V-shaped cycle lows (sharp decline followed by sharp rise).
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Parameters:
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- window: Window to look for sharp price changes
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- threshold: Percentage change threshold to consider 'sharp'
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Returns:
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- list: Indices where V-shaped patterns are detected
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"""
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patterns = []
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# Find all swing lows
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swing_low_indices = np.where(self.data['swing_low'])[0]
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for idx in swing_low_indices:
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# Need enough data points before and after
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if idx < window or idx + window >= len(self.data):
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continue
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# Get the low price at this swing low
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low_price = self.data.iloc[idx]['low']
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# Check for sharp decline before low (at least window bars before)
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before_segment = self.data.iloc[max(0, idx-window):idx]
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if len(before_segment) > 0:
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max_before = before_segment['high'].max()
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decline = (max_before - low_price) / max_before
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# Check for sharp rise after low (at least window bars after)
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after_segment = self.data.iloc[idx+1:min(len(self.data), idx+window+1)]
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if len(after_segment) > 0:
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max_after = after_segment['high'].max()
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rise = (max_after - low_price) / low_price
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# Both decline and rise must exceed threshold to be considered V-shaped
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if decline > threshold and rise > threshold:
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patterns.append(idx)
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return patterns
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def plot_cycles(self, pattern_detection=None, title_suffix=''):
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"""
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Plot the price data with cycle lows and detected patterns.
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Parameters:
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- pattern_detection: 'two_drives', 'v_shape', or None
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- title_suffix: Optional suffix for the plot title
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"""
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plt.figure(figsize=(14, 7))
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# Determine the date column name (could be 'date' or 'datetime')
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date_col = 'date' if 'date' in self.data.columns else 'datetime'
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# Plot price data
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plt.plot(self.data[date_col], self.data['close'], label='Close Price')
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# Calculate a consistent vertical position for indicators based on price range
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price_range = self.data['close'].max() - self.data['close'].min()
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indicator_offset = price_range * 0.01 # 1% of price range
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# Plot cycle lows (now at a fixed offset below the low price)
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swing_lows = self.data[self.data['swing_low']]
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plt.scatter(swing_lows[date_col], swing_lows['low'] - indicator_offset,
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color='green', marker='^', s=100, label='Cycle Lows')
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# Plot specific patterns if requested
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if 'two_drives' in pattern_detection:
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pattern_indices = self.detect_two_drives_pattern()
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if pattern_indices:
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patterns = self.data.iloc[pattern_indices]
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plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2,
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color='red', marker='o', s=150, label='Two Drives Pattern')
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elif 'v_shape' in pattern_detection:
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pattern_indices = self.detect_v_shaped_lows()
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if pattern_indices:
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patterns = self.data.iloc[pattern_indices]
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plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2,
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color='purple', marker='o', s=150, label='V-Shape Pattern')
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# Add cycle lengths and averages
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cycle_lengths = self.calculate_cycle_lengths()
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avg_cycle = self.get_average_cycle_length()
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cycle_window = self.get_cycle_window()
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window_text = ""
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if cycle_window:
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window_text = f"Tolerance Window: [{cycle_window[0]:.2f} - {cycle_window[2]:.2f}]"
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plt.title(f"Detected Cycles - {self.timeframe.capitalize()} Timeframe {title_suffix}\n"
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f"Average Cycle Length: {avg_cycle:.2f} periods, {window_text}")
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plt.legend()
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plt.grid(True)
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plt.show()
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# Usage example:
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# 1. Load your data
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# data = pd.read_csv('your_price_data.csv')
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# 2. Create cycle detector instances for different timeframes
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# weekly_detector = CycleDetector(data, timeframe='weekly')
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# daily_detector = CycleDetector(data, timeframe='daily')
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# 3. Analyze cycles
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# weekly_cycle_length = weekly_detector.get_average_cycle_length()
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# daily_cycle_length = daily_detector.get_average_cycle_length()
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# 4. Detect patterns
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# two_drives = weekly_detector.detect_two_drives_pattern()
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# v_shapes = daily_detector.detect_v_shaped_lows()
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# 5. Visualize
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# weekly_detector.plot_cycles(pattern_detection='two_drives')
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# daily_detector.plot_cycles(pattern_detection='v_shape')
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BIN
requirements.txt
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requirements.txt
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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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from concurrent.futures import ProcessPoolExecutor, as_completed
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import concurrent.futures
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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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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")
|
|
||||||
trends = []
|
|
||||||
trend_start = 0
|
|
||||||
|
|
||||||
for i in range(1, len(df)):
|
|
||||||
|
|
||||||
if df['trend_change'].iloc[i]:
|
|
||||||
if i > trend_start:
|
|
||||||
trends.append({
|
|
||||||
"type": df['trend'].iloc[i-1],
|
|
||||||
"start_index": trend_start,
|
|
||||||
"end_index": i-1,
|
|
||||||
"start_value": df['close'].iloc[trend_start],
|
|
||||||
"end_value": df['close'].iloc[i-1]
|
|
||||||
})
|
|
||||||
trend_start = i
|
|
||||||
|
|
||||||
# Add the last trend
|
|
||||||
if trend_start < len(df):
|
|
||||||
trends.append({
|
|
||||||
"type": df['trend'].iloc[-1],
|
|
||||||
"start_index": trend_start,
|
|
||||||
"end_index": len(df)-1,
|
|
||||||
"start_value": df['close'].iloc[trend_start],
|
|
||||||
"end_value": df['close'].iloc[-1]
|
|
||||||
})
|
|
||||||
|
|
||||||
self.logger.info(f"Detected {len(trends)} trend segments")
|
|
||||||
return trends
|
|
||||||
|
|
||||||
def get_strongest_trend(self):
|
|
||||||
self.logger.info("Finding strongest trend")
|
|
||||||
trends = self.detect_trends_MACD_signal()
|
|
||||||
if isinstance(trends, dict) and "error" in trends:
|
|
||||||
self.logger.warning(f"Error in trend detection: {trends['error']}")
|
|
||||||
return trends
|
|
||||||
|
|
||||||
if not trends:
|
|
||||||
self.logger.info("No significant trends detected")
|
|
||||||
return {"message": "No significant trends detected"}
|
|
||||||
|
|
||||||
strongest = max(trends, key=lambda x: abs(x["end_value"] - x["start_value"]))
|
|
||||||
self.logger.info(f"Strongest trend: {strongest['type']} from index {strongest['start_index']} to {strongest['end_index']}")
|
|
||||||
return strongest
|
|
||||||
|
|
||||||
def plot_trends(self, trends):
|
|
||||||
"""
|
|
||||||
Plot price data with identified trends highlighted using candlestick charts.
|
|
||||||
"""
|
|
||||||
self.logger.info("Plotting trends with candlesticks")
|
|
||||||
if isinstance(trends, dict) and "error" in trends:
|
|
||||||
self.logger.error(trends["error"])
|
|
||||||
print(trends["error"])
|
|
||||||
return
|
|
||||||
|
|
||||||
if not trends:
|
|
||||||
self.logger.warning("No significant trends detected for plotting")
|
|
||||||
print("No significant trends detected")
|
|
||||||
return
|
|
||||||
|
|
||||||
# Create a figure with 2 subplots that share the x-axis
|
|
||||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={'height_ratios': [2, 1]}, sharex=True)
|
|
||||||
self.logger.info("Creating plot figure with shared x-axis")
|
|
||||||
|
|
||||||
# Prepare data for candlestick chart
|
|
||||||
df = self.data.copy()
|
|
||||||
|
|
||||||
# Ensure required columns exist for candlestick
|
|
||||||
required_cols = ['open', 'high', 'low', 'close']
|
|
||||||
if not all(col in df.columns for col in required_cols):
|
|
||||||
self.logger.warning("Missing required columns for candlestick. Defaulting to line chart.")
|
|
||||||
if 'close' in df.columns:
|
|
||||||
ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'],
|
|
||||||
df['close'], color='black', alpha=0.7, linewidth=1, label='Price')
|
|
||||||
else:
|
|
||||||
ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'],
|
|
||||||
df[df.columns[0]], color='black', alpha=0.7, linewidth=1, label='Price')
|
|
||||||
else:
|
|
||||||
# Get x values (dates if available, otherwise indices)
|
|
||||||
if 'datetime' in df.columns:
|
|
||||||
x_label = 'Date'
|
|
||||||
# Format date axis
|
|
||||||
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
|
|
||||||
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
|
|
||||||
fig.autofmt_xdate()
|
|
||||||
self.logger.info("Using datetime for x-axis")
|
|
||||||
|
|
||||||
# For candlestick, ensure datetime is the index
|
|
||||||
if df.index.name != 'datetime':
|
|
||||||
df = df.set_index('datetime')
|
|
||||||
else:
|
|
||||||
x_label = 'Index'
|
|
||||||
self.logger.info("Using index for x-axis")
|
|
||||||
|
|
||||||
# Plot candlestick chart
|
|
||||||
up_color = 'green'
|
|
||||||
down_color = 'red'
|
|
||||||
|
|
||||||
# Draw candlesticks manually
|
|
||||||
width = 0.6
|
|
||||||
for i in range(len(df)):
|
|
||||||
# Get OHLC values for this candle
|
|
||||||
open_val = df['open'].iloc[i]
|
|
||||||
close_val = df['close'].iloc[i]
|
|
||||||
high_val = df['high'].iloc[i]
|
|
||||||
low_val = df['low'].iloc[i]
|
|
||||||
idx = df.index[i]
|
|
||||||
|
|
||||||
# Determine candle color
|
|
||||||
color = up_color if close_val >= open_val else 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)
|
|
||||||
ax1.add_patch(rect)
|
|
||||||
|
|
||||||
# Plot candle wicks
|
|
||||||
ax1.plot([i, i], [low_val, high_val], color='black', linewidth=1)
|
|
||||||
|
|
||||||
# Set appropriate x-axis limits
|
|
||||||
ax1.set_xlim(-0.5, len(df) - 0.5)
|
|
||||||
|
|
||||||
# Highlight each trend with a different color
|
|
||||||
self.logger.info("Highlighting trends on plot")
|
|
||||||
for trend in trends:
|
|
||||||
start_idx = trend['start_index']
|
|
||||||
end_idx = trend['end_index']
|
|
||||||
trend_type = trend['type']
|
|
||||||
|
|
||||||
# Get x-coordinates for trend plotting
|
|
||||||
x_start = start_idx
|
|
||||||
x_end = end_idx
|
|
||||||
|
|
||||||
# Get y-coordinates for trend line
|
|
||||||
if 'close' in df.columns:
|
|
||||||
y_start = df['close'].iloc[start_idx]
|
|
||||||
y_end = df['close'].iloc[end_idx]
|
|
||||||
else:
|
|
||||||
y_start = df[df.columns[0]].iloc[start_idx]
|
|
||||||
y_end = df[df.columns[0]].iloc[end_idx]
|
|
||||||
|
|
||||||
# Choose color based on trend type
|
|
||||||
color = 'green' if trend_type == 'up' else 'red'
|
|
||||||
|
|
||||||
# Plot trend line
|
|
||||||
ax1.plot([x_start, x_end], [y_start, y_end], color=color, linewidth=2,
|
|
||||||
label=f"{trend_type.capitalize()} Trend" if f"{trend_type.capitalize()} Trend" not in ax1.get_legend_handles_labels()[1] else "")
|
|
||||||
|
|
||||||
# Add markers at start and end points
|
|
||||||
ax1.scatter(x_start, y_start, color=color, marker='o', s=50)
|
|
||||||
ax1.scatter(x_end, y_end, color=color, marker='s', s=50)
|
|
||||||
|
|
||||||
# Configure first subplot
|
|
||||||
ax1.set_title('Price with Trends (Candlestick)', fontsize=16)
|
|
||||||
ax1.set_ylabel('Price', fontsize=14)
|
|
||||||
ax1.grid(alpha=0.3)
|
|
||||||
ax1.legend()
|
|
||||||
|
|
||||||
# Create MACD in second subplot
|
|
||||||
self.logger.info("Creating MACD subplot")
|
|
||||||
|
|
||||||
# Calculate MACD indicators if not already present
|
|
||||||
if 'macd' not in df.columns:
|
|
||||||
if 'close' not in df.columns and len(df.columns) > 0:
|
|
||||||
df['close'] = df[df.columns[0]]
|
|
||||||
|
|
||||||
df['macd'] = ta.trend.macd(df['close'])
|
|
||||||
df['macd_signal'] = ta.trend.macd_signal(df['close'])
|
|
||||||
df['macd_diff'] = ta.trend.macd_diff(df['close'])
|
|
||||||
|
|
||||||
# Plot MACD components on second subplot
|
|
||||||
x_indices = np.arange(len(df))
|
|
||||||
ax2.plot(x_indices, df['macd'], label='MACD', color='blue')
|
|
||||||
ax2.plot(x_indices, df['macd_signal'], label='Signal', color='orange')
|
|
||||||
|
|
||||||
# Plot MACD histogram
|
|
||||||
for i in range(len(df)):
|
|
||||||
if df['macd_diff'].iloc[i] >= 0:
|
|
||||||
ax2.bar(i, df['macd_diff'].iloc[i], color='green', alpha=0.5, width=0.8)
|
|
||||||
else:
|
|
||||||
ax2.bar(i, df['macd_diff'].iloc[i], color='red', alpha=0.5, width=0.8)
|
|
||||||
|
|
||||||
ax2.set_title('MACD Indicator', fontsize=16)
|
|
||||||
ax2.set_xlabel(x_label, fontsize=14)
|
|
||||||
ax2.set_ylabel('MACD', fontsize=14)
|
|
||||||
ax2.grid(alpha=0.3)
|
|
||||||
ax2.legend()
|
|
||||||
|
|
||||||
# Enable synchronized zooming
|
|
||||||
plt.tight_layout()
|
|
||||||
plt.subplots_adjust(hspace=0.1)
|
|
||||||
plt.show()
|
|
||||||
|
|
||||||
return plt
|
|
||||||
|
|
||||||
def _calculate_supertrend_indicators(self):
|
|
||||||
"""
|
|
||||||
Calculate SuperTrend indicators with different parameter sets in parallel.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- list, the SuperTrend results
|
|
||||||
"""
|
|
||||||
supertrend_params = [
|
|
||||||
{"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
|
||||||
{"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
|
||||||
{"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
|
|
||||||
]
|
|
||||||
|
|
||||||
def run_supertrend(params):
|
|
||||||
# Each thread gets its own copy of the data to avoid race conditions
|
|
||||||
return {
|
|
||||||
"results": self.calculate_supertrend(
|
|
||||||
period=params["period"],
|
|
||||||
multiplier=params["multiplier"]
|
|
||||||
),
|
|
||||||
"params": params
|
|
||||||
}
|
|
||||||
|
|
||||||
with concurrent.futures.ThreadPoolExecutor() as executor:
|
|
||||||
results = list(executor.map(run_supertrend, supertrend_params))
|
|
||||||
|
|
||||||
return results
|
|
||||||
Loading…
x
Reference in New Issue
Block a user