105 lines
4.4 KiB
Python
105 lines
4.4 KiB
Python
import matplotlib
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import matplotlib.dates as mdates
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matplotlib.use('Agg') # Set the Matplotlib backend to 'Agg'
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MaxNLocator
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import seaborn as sns
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import pandas as pd
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from datetime import datetime
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from typing import List
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from enum import Enum
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import numpy as np
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import v2realbot.controller.services as cs
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from rich import print
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from v2realbot.common.model import AnalyzerInputs
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from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
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from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
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from pathlib import Path
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from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
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from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
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from io import BytesIO
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from v2realbot.utils.historicals import get_historical_bars
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from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
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from collections import defaultdict
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from scipy.stats import zscore
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from io import BytesIO
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from v2realbot.reporting.load_trades import load_trades
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from typing import Tuple, Optional, List
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from traceback import format_exc
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import pandas as pd
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def daily_profit_distribution(runner_ids: list = None, batch_id: str = None, stream: bool = False):
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try:
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res, trades, days_cnt = load_trades(runner_ids, batch_id)
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if res != 0:
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raise Exception("Error in loading trades")
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#print(trades)
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# Convert list of Trade objects to DataFrame
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trades_df = pd.DataFrame([t.__dict__ for t in trades if t.status == "closed"])
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# Ensure 'exit_time' is a datetime object and make it timezone-naive if necessary
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trades_df['exit_time'] = pd.to_datetime(trades_df['exit_time']).dt.tz_convert(zoneNY)
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trades_df['date'] = trades_df['exit_time'].dt.date
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daily_profit = trades_df.groupby(['date', 'direction']).profit.sum().unstack(fill_value=0)
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#print("dp",daily_profit)
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daily_cumulative_profit = trades_df.groupby('date').profit.sum().cumsum()
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# Create the plot
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fig, ax1 = plt.subplots(figsize=(10, 6))
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# Bar chart for daily profit composition
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daily_profit.plot(kind='bar', stacked=True, ax=ax1, color=['green', 'red'], zorder=2)
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ax1.set_ylabel('Daily Profit')
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ax1.set_xlabel('Date')
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#ax1.xaxis.set_major_locator(MaxNLocator(10))
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# Line chart for cumulative daily profit
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#ax2 = ax1.twinx()
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#print(daily_cumulative_profit)
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#print(daily_cumulative_profit.index)
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#ax2.plot(daily_cumulative_profit.index, daily_cumulative_profit, color='yellow', linestyle='-', linewidth=2, zorder=3)
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#ax2.set_ylabel('Cumulative Profit')
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# Setting the secondary y-axis range dynamically based on cumulative profit values
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# ax2.set_ylim(daily_cumulative_profit.min() - (daily_cumulative_profit.std() * 2),
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# daily_cumulative_profit.max() + (daily_cumulative_profit.std() * 2))
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# Dark mode settings
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ax1.set_facecolor('black')
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# ax1.grid(True)
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#ax2.set_facecolor('black')
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fig.patch.set_facecolor('black')
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ax1.tick_params(colors='white')
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#ax2.tick_params(colors='white')
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# ax1.xaxis_date()
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# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.', tz=zoneNY))
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ax1.tick_params(axis='x', rotation=45)
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# Footer
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footer_text = f'Days Count: {days_cnt} | Parameters: {{"runner_ids": {len(runner_ids) if runner_ids is not None else None}, "batch_id": {batch_id}, "stream": {stream}}}'
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plt.figtext(0.5, 0.01, footer_text, wrap=True, horizontalalignment='center', fontsize=8, color='white')
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# Save or stream the plot
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if stream:
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img_stream = BytesIO()
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plt.savefig(img_stream, format='png', bbox_inches='tight', facecolor=fig.get_facecolor(), edgecolor='none')
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img_stream.seek(0)
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plt.close(fig)
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return (0, img_stream)
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else:
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plt.savefig(f'{__name__}.png', bbox_inches='tight', facecolor=fig.get_facecolor(), edgecolor='none')
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plt.close(fig)
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return (0, None)
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except Exception as e:
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# Detailed error reporting
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return (-1, str(e) + format_exc())
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# Local debugging
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if __name__ == '__main__':
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batch_id = "6f9b012c"
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res, val = daily_profit_distribution(batch_id=batch_id)
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print(res, val)
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