bugfix create batch image (check for None from Alpaca)
This commit is contained in:
David Brazda
@ -1,20 +1,585 @@
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import argparse
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import v2realbot.reporting.metricstoolsimage as mt
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#import v2realbot.reporting.metricstoolsimage as mt
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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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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.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
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from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get
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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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import mlroom.utils.ext_services as es
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from v2realbot.common.db import pool, execute_with_retry, row_to_runarchive, row_to_runarchiveview
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from v2realbot.utils.utils import ltp, isrising, isfalling,trunc,AttributeDict
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import tqdm
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# start_date = datetime(2020, 1, 1, 0, 0, 0, 0, zoneNY)
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# end_date = datetime(2024, 1, 2, 0, 0, 0, 0, zoneNY)
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# bars= get_historical_bars("BLK", start_date, end_date, TimeFrame.Hour)
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# print("bars for given period",bars)
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#upload image to the remote server scp 4bea3a54.png david@142.132.188.109:/home/david/media/basic/
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#zkopirovany a upravena funkce, ktera umi vybrat cilovy server
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def generate_trading_report_image(runner_ids: list = None, batch_id: str = None, stream: bool = False, server: str = None):
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#TODO dopracovat drawdown a minimalni a maximalni profity nikoliv cumulovane, zamyslet se
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#TODO list of runner_ids
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#TODO pridelat na vytvoreni runnera a batche, samostatne REST API + na remove archrunnera
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if runner_ids is None and batch_id is None:
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return -2, f"runner_id or batch_id must be present"
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if batch_id is not None:
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res, runner_ids = es.get_archived_runners_list_by_batch_id(batch_id, server)
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if res != 0:
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print(f"no batch {batch_id} found")
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return -1, f"no batch {batch_id} found"
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trades = []
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symbol = None
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mode = None
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sada_list = []
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for id in tqdm.tqdm(runner_ids):
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#get runner
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res, sada =es.get_archived_runner_header_by_id(id, server)
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if res != 0:
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print(f"no runner {id} found")
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return -1, f"no runner {id} found"
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#sada = AttributeDict(**sada)
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print("archrunner")
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print(sada)
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sada["started"]=datetime.fromisoformat(sada['started']) if sada['started'] else None
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sada["stopped"]=datetime.fromisoformat(sada['stopped']) if sada['stopped'] else None
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sada["bt_from"]=datetime.fromisoformat(sada['bt_from']) if sada['bt_from'] else None
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sada["bt_to"]=datetime.fromisoformat(sada['bt_to']) if sada['bt_to'] else None
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sada_list.append(sada)
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symbol = sada["symbol"]
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mode = sada["mode"]
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# Parse trades
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trades_dicts = sada["metrics"]["prescr_trades"]
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for trade_dict in trades_dicts:
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trade_dict['last_update'] = datetime.fromtimestamp(trade_dict.get('last_update')).astimezone(zoneNY) if trade_dict['last_update'] is not None else None
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trade_dict['entry_time'] = datetime.fromtimestamp(trade_dict.get('entry_time')).astimezone(zoneNY) if trade_dict['entry_time'] is not None else None
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trade_dict['exit_time'] = datetime.fromtimestamp(trade_dict.get('exit_time')).astimezone(zoneNY) if trade_dict['exit_time'] is not None else None
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trades.append(Trade(**trade_dict))
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#print(trades)
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#get from to dates
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#calculate start a end z min a max dni - jelikoz muze byt i seznam runner_ids a nejenom batch, pripadne testovaci sady
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if mode in [Mode.BT,Mode.PREP]:
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start_date = min(runner["bt_from"] for runner in sada_list)
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end_date = max(runner["bt_to"] for runner in sada_list)
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else:
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start_date = min(runner["started"] for runner in sada_list)
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end_date = max(runner["stopped"] for runner in sada_list)
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#hour bars for backtested period
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print(start_date,end_date)
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bars= get_historical_bars(symbol, start_date, end_date, TimeFrame.Hour)
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print("bars for given period",bars)
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"""Bars a dictionary with the following keys:
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* high: A list of high prices
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* low: A list of low prices
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* volume: A list of volumes
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* close: A list of close prices
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* hlcc4: A list of HLCC4 indicators
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* open: A list of open prices
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* time: A list of times in UTC (ISO 8601 format)
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* trades: A list of number of trades
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* resolution: A list of resolutions (all set to 'D')
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* confirmed: A list of booleans (all set to True)
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* vwap: A list of VWAP indicator
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* updated: A list of booleans (all set to True)
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* index: A list of integers (from 0 to the length of the list of daily bars)
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"""
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# Filter to only use trades with status 'CLOSED'
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closed_trades = [trade for trade in trades if trade.status == TradeStatus.CLOSED]
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if len(closed_trades) == 0:
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return -1, "image generation no closed trades"
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# Data extraction for the plots
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exit_times = [trade.exit_time for trade in closed_trades if trade.exit_time is not None]
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##cumulative_profits = [trade.profit_sum for trade in closed_trades if trade.profit_sum is not None]
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profits = [trade.profit for trade in closed_trades if trade.profit is not None]
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cumulative_profits = np.cumsum(profits)
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wins = [trade.profit for trade in closed_trades if trade.profit > 0]
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losses = [trade.profit for trade in closed_trades if trade.profit < 0]
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wins_long = [trade.profit for trade in closed_trades if trade.profit > 0 and trade.direction == TradeDirection.LONG]
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losses_long = [trade.profit for trade in closed_trades if trade.profit < 0 and trade.direction == TradeDirection.LONG]
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wins_short = [trade.profit for trade in closed_trades if trade.profit > 0 and trade.direction == TradeDirection.SHORT]
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losses_short = [trade.profit for trade in closed_trades if trade.profit < 0 and trade.direction == TradeDirection.SHORT]
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directions = [trade.direction for trade in closed_trades]
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long_profits = [trade.profit for trade in closed_trades if trade.direction == TradeDirection.LONG and trade.profit is not None]
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short_profits = [trade.profit for trade in closed_trades if trade.direction == TradeDirection.SHORT and trade.profit is not None]
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# Setting up dark mode for the plots
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plt.style.use('dark_background')
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# Optionally, you can further customize colors, labels, and axes
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params = {
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'axes.titlesize': 9,
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'axes.labelsize': 8,
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'xtick.labelsize': 9,
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'ytick.labelsize': 9,
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'axes.labelcolor': '#a9a9a9', #a1a3aa',
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'axes.facecolor': '#121722', #'#0e0e0e', #202020', # Dark background for plot area
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'axes.grid': False, # Turn off the grid globally
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'grid.color': 'gray', # If the grid is on, set grid line color
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'grid.linestyle': '--', # Grid line style
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'grid.linewidth': 1,
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'xtick.color': '#a9a9a9',
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'ytick.color': '#a9a9a9',
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'axes.edgecolor': '#a9a9a9'
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}
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plt.rcParams.update(params)
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#Custom dark theme similar to the provided image
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# dark_finance_theme = {
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# 'background': '#1a1a1a', # Very dark (almost black) background
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# 'text': '#eaeaea', # Light grey text for readability
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# 'grid': '#333333', # Dark grey grid lines
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# 'accent': '#2e91e5', # Bright blue accent for main elements
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# 'secondary': '#e15f99', # Secondary pink/magenta color for highlights
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# 'highlight': '#fcba03', # Gold-like color for special highlights
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# }
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# # Apply the theme settings
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# plt.style.use('dark_background')
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# plt.rcParams.update({
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# 'figure.facecolor': dark_finance_theme['background'],
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# 'axes.facecolor': dark_finance_theme['background'],
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# 'axes.edgecolor': dark_finance_theme['text'],
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# 'axes.labelcolor': dark_finance_theme['text'],
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# 'axes.titlesize': 12,
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# 'axes.labelsize': 10,
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# 'xtick.color': dark_finance_theme['text'],
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# 'xtick.labelsize': 8,
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# 'ytick.color': dark_finance_theme['text'],
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# 'ytick.labelsize': 8,
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# 'grid.color': dark_finance_theme['grid'],
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# 'grid.linestyle': '-',
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# 'grid.linewidth': 0.6,
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# 'legend.facecolor': dark_finance_theme['background'],
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# 'legend.edgecolor': dark_finance_theme['background'],
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# 'legend.fontsize': 10,
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# 'text.color': dark_finance_theme['text'],
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# 'lines.color': dark_finance_theme['accent'],
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# 'patch.edgecolor': dark_finance_theme['accent'],
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# })
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if len(closed_trades) > 100:
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fig, axs = plt.subplots(3, 4, figsize=(15, 10))
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else:
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# Create a combined figure for all plots 11,7 ideal na 3,4
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fig, axs = plt.subplots(3, 4, figsize=(12, 7))
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#TITLE
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title = ""
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cnt_ids = len(runner_ids)
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if batch_id is not None:
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title = "Batch: "+str(batch_id)+ " "
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title += "Days: " + str(cnt_ids)
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if cnt_ids == 1:
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title += " ("+str(runner_ids[0])[0:14]+") "
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if sada["mode"] == Mode.BT:
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datum = sada["bt_from"]
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else:
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datum = sada["started"]
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title += datum.strftime("%d.%m.%Y %H:%M")
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# Add a title to the figure
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fig.suptitle(title, fontsize=15, color='white')
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# Plot 1: Overall Profit Summary Chart
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total_wins = int(sum(wins))
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total_losses = int(sum(losses))
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net_profit = int(sum(profits))
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sns.barplot(x=['Total', 'Wins','Losses'],
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y=[net_profit, total_wins, total_losses],
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ax=axs[0, 0])
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axs[0, 0].set_title('Overall Profit Summary')
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# Define the offset for placing text inside the bars
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offset = max(total_wins, abs(total_losses), net_profit) * 0.05 # 5% of the highest (or lowest) bar value
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# Function to place text annotation
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def place_annotation(ax, x, value, offset):
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va = 'top' if value >= 0 else 'bottom'
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y = value - offset if value >= 0 else value + offset
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ax.text(x, y, f'{value}', ha='center', va=va, color='black', fontsize=12)
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# Annotate the Total Wins, Losses, and Net Profit bars
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place_annotation(axs[0, 0], 0, net_profit, offset)
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place_annotation(axs[0, 0], 1, total_wins, offset)
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place_annotation(axs[0, 0], 2, total_losses, offset)
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# Plot 2: LONG - profit summary
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total_wins_long = int(sum(wins_long))
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total_losses_long = int(sum(losses_long))
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total_long = total_wins_long + total_losses_long
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sns.barplot(x=['Total', 'Wins','Losses'],
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y=[total_long, total_wins_long, total_losses_long],
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ax=axs[0, 1])
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axs[0, 1].set_title('LONG Profit Summary')
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# Define the offset for placing text inside the bars
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offset = max(total_wins_long, abs(total_losses_long)) * 0.05 # 5% of the highest (or lowest) bar value
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place_annotation(axs[0, 1], 0, total_long, offset)
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place_annotation(axs[0, 1], 1, total_wins_long, offset)
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place_annotation(axs[0, 1], 2, total_losses_long, offset)
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# Plot 3: SHORT - profit summary
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total_wins_short =int(sum(wins_short))
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total_losses_short = int(sum(losses_short))
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total_short = total_wins_short + total_losses_short
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sns.barplot(x=['Total', 'Wins', 'Losses'],
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y=[total_short, total_wins_short,
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total_losses_short],
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ax=axs[0, 2])
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axs[0, 2].set_title('SHORT Profit Summary')
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# Define the offset for placing text inside the bars
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offset = max(total_wins_short, abs(total_losses_short)) * 0.05 # 5% of the highest (or lowest) bar value
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place_annotation(axs[0, 2], 0, total_short, offset)
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place_annotation(axs[0, 2], 1, total_wins_short, offset)
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place_annotation(axs[0, 2], 2, total_losses_short, offset)
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# Plot 4: Trade Counts Bar Chart
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long_count = len([trade for trade in closed_trades if trade.direction == TradeDirection.LONG])
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short_count = len([trade for trade in closed_trades if trade.direction == TradeDirection.SHORT])
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sns.barplot(x=['Long Trades', 'Short Trades'], y=[long_count, short_count], ax=axs[0, 3])
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axs[0, 3].set_title('Trade Counts')
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offset = max(long_count, short_count) * 0.05 # 5% of the highest (or lowest) bar value
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place_annotation(axs[0, 3], 0, long_count, offset)
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place_annotation(axs[0, 3], 1, short_count, offset)
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#PLOT 5 - Heatman (exit time)
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# Creating a DataFrame for the heatmap
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heatmap_data_list = []
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for trade in trades:
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if trade.status == TradeStatus.CLOSED:
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day = trade.exit_time.strftime('%m-%d') # Format date as 'MM-DD'
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#day = trade.exit_time.date()
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hour = trade.exit_time.hour
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profit = trade.profit
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heatmap_data_list.append({'Day': day, 'Hour': hour, 'Profit': profit})
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try:
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heatmap_data = pd.DataFrame(heatmap_data_list)
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heatmap_data = heatmap_data.groupby(['Day', 'Hour']).sum().reset_index()
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heatmap_pivot = heatmap_data.pivot(index='Day', columns='Hour', values='Profit')
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# Heatmap of Profits
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sns.heatmap(heatmap_pivot, cmap='viridis', ax=axs[1, 0])
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axs[1, 0].set_title('Heatmap of Profits (based on Exit time)')
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axs[1, 0].set_xlabel('Hour of Day')
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axs[1, 0].set_ylabel('Day')
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except KeyError:
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# Handle the case where there is no data
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axs[1, 0].text(0.5, 0.5, 'No data available',
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horizontalalignment='center',
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verticalalignment='center',
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transform=axs[1, 0].transAxes)
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axs[1, 0].set_title('Heatmap of Profits (based on Exit time)')
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# Plot 6: Profit/Loss Distribution Histogram
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sns.histplot(profits, bins=30, ax=axs[1, 1], kde=True, color='skyblue')
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axs[1, 1].set_title('Profit/Loss Distribution')
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axs[1, 1].set_xlabel('Profit/Loss')
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axs[1, 1].set_ylabel('Frequency')
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# Plot 7
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# - for 1 den: Position Size Distribution
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# - for more days: Trade Duration vs. Profit/Loss
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if len(runner_ids) == 1:
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sizes = [trade.size for trade in closed_trades if trade.size is not None]
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if sizes:
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size_counts = {size: sizes.count(size) for size in set(sizes)}
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sns.barplot(x=list(size_counts.keys()), y=list(size_counts.values()), ax=axs[1, 2])
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axs[1, 2].set_title('Position Size Distribution')
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else:
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# Handle the case where there is no data
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axs[1, 2].text(0.5, 0.5, 'No data available',
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horizontalalignment='center',
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verticalalignment='center',
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transform=axs[1, 2].transAxes)
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axs[1, 2].set_title('Position Size Distribution')
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else:
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trade_durations = []
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trade_profits = []
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#trade_volumes = [] # Assuming you have a way to measure the size/volume of each trade
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trade_types = [] # 'Long' or 'Short'
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for trade in trades:
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if trade.status == TradeStatus.CLOSED:
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duration = (trade.exit_time - trade.entry_time).total_seconds() / 60 # Duration in minutes (3600 for hours)
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trade_durations.append(duration)
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trade_profits.append(trade.profit)
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##trade_volumes.append(trade.size) # or any other measure of trade size
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trade_types.append('Long' if trade.direction == TradeDirection.LONG else 'Short')
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# Trade Duration vs. Profit/Loss
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scatter_data = pd.DataFrame({
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'Duration': trade_durations,
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'Profit': trade_profits,
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#'Volume': trade_volumes,
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'Type': trade_types
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})
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#sns.scatterplot(data=scatter_data, x='Duration', y='Profit', size='Volume', hue='Type', ax=axs[1, 2])
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sns.scatterplot(data=scatter_data, x='Duration', y='Profit', hue='Type', ax=axs[1, 2])
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axs[1, 2].set_title('Trade Duration vs. Profit/Loss')
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axs[1, 2].set_xlabel('Duration (Minutes)')
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axs[1, 2].set_ylabel('Profit/Loss')
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#Plot 8 Cumulative profit - bud 1 den nebo vice dni + pridame pod to vyvoj ceny
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# Extract the closing prices and times
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closing_prices = bars.get('close',[]) if bars is not None else []
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#times = bars['time'] # Assuming this is a list of pandas Timestamp objects
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times = pd.to_datetime(bars['time']) if bars is not None else [] # Ensure this is a Pandas datetime series
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# # Plot the closing prices over time
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# axs[0, 4].plot(times, closing_prices, color='blue')
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# axs[0, 4].tick_params(axis='x', rotation=45) # Rotate date labels if necessar
|
||||
# axs[0, 4].xaxis.set_major_formatter(mdates.DateFormatter('%H', tz=zoneNY))
|
||||
|
||||
if len(runner_ids)== 1:
|
||||
if cumulative_profits.size > 0:
|
||||
# Plot 3: Cumulative Profit Over Time with Max Profit Point
|
||||
max_profit_time = exit_times[np.argmax(cumulative_profits)]
|
||||
max_profit = max(cumulative_profits)
|
||||
min_profit_time = exit_times[np.argmin(cumulative_profits)]
|
||||
min_profit = min(cumulative_profits)
|
||||
|
||||
#Plot Cumulative Profit Over Time with Max Profit Point on the primary y-axis
|
||||
# Create a secondary y-axis for the closing prices
|
||||
ax2 = axs[1, 3].twinx()
|
||||
ax2.plot(times, closing_prices, label='Closing Price', color='orange')
|
||||
ax2.set_ylabel('Closing Price', color='orange')
|
||||
ax2.tick_params(axis='y', labelcolor='orange')
|
||||
|
||||
# Set the limits for the x-axis to cover the full range of 'times'
|
||||
if isinstance(times, pd.DatetimeIndex):
|
||||
axs[1, 3].set_xlim(times.min(), times.max())
|
||||
sns.lineplot(x=exit_times, y=cumulative_profits, ax=axs[1, 3], color='limegreen')
|
||||
axs[1, 3].scatter(max_profit_time, max_profit, color='green', label='Max Profit')
|
||||
axs[1, 3].scatter(min_profit_time, min_profit, color='red', label='Min Profit')
|
||||
axs[1, 3].set_xlabel('Time')
|
||||
axs[1, 3].set_ylabel('Cumulative Profit', color='limegreen')
|
||||
axs[1, 3].tick_params(axis='y', labelcolor='limegreen')
|
||||
axs[1, 3].xaxis.set_major_formatter(mdates.DateFormatter('%H', tz=zoneNY))
|
||||
# Add legends to the plot
|
||||
# lines, labels = axs[1, 3].get_legend_handles_labels()
|
||||
# lines2, labels2 = ax2.get_legend_handles_labels()
|
||||
# axs[1, 3].legend(lines + lines2, labels + labels2, loc='upper left')
|
||||
else:
|
||||
# Handle the case where cumulative_profits is empty
|
||||
axs[1, 3].text(0.5, 0.5, 'No profit data available',
|
||||
horizontalalignment='center',
|
||||
verticalalignment='center',
|
||||
transform=axs[1, 3].transAxes)
|
||||
axs[1, 3].set_title('Cumulative Profit Over Time')
|
||||
else:
|
||||
# Calculate cumulative profit
|
||||
# Additional Plot: Cumulative Profit Over Time
|
||||
# Sort trades by exit time
|
||||
|
||||
# # Set the limits for the x-axis to cover the full range of 'times'
|
||||
# axs[1, 3].set_xlim(times.min(), times.max())
|
||||
|
||||
sorted_trades = sorted([trade for trade in trades if trade.status == TradeStatus.CLOSED],
|
||||
key=lambda x: x.exit_time)
|
||||
cumulative_profits_sorted = np.cumsum([trade.profit for trade in sorted_trades])
|
||||
exit_times_sorted = [trade.exit_time for trade in sorted_trades if trade.exit_time is not None]
|
||||
|
||||
# Create a secondary y-axis for the closing prices
|
||||
ax2 = axs[1, 3].twinx()
|
||||
ax2.plot(times, closing_prices, label='Closing Price', color='orange')
|
||||
ax2.set_ylabel('Closing Price', color='orange')
|
||||
ax2.tick_params(axis='y', labelcolor='orange')
|
||||
|
||||
axs[1, 3].set_xlim(times.min(), times.max())
|
||||
# Plot Cumulative Profit Over Time on the primary y-axis
|
||||
axs[1, 3].plot(exit_times_sorted, cumulative_profits_sorted, label='Cumulative Profit', color='blue')
|
||||
axs[1, 3].set_xlabel('Time')
|
||||
axs[1, 3].set_ylabel('Cumulative Profit', color='blue')
|
||||
axs[1, 3].tick_params(axis='y', labelcolor='blue')
|
||||
|
||||
# Format dates on the x-axis
|
||||
axs[1, 3].xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.', tz=zoneNY))
|
||||
axs[1, 3].tick_params(axis='x', rotation=45) # Rotate date labels if necessary
|
||||
|
||||
# Set the title
|
||||
axs[1, 3].set_title('Cumulative Profit and Closing Price Over Time')
|
||||
|
||||
# Add legends to the plot
|
||||
# axs[1, 3].legend(loc='upper left')
|
||||
# ax2.legend(loc='upper right')
|
||||
|
||||
# Plot 9
|
||||
# - for 1 day: Daily Relative Profit Chart
|
||||
# - for more days: Heatmap of Profits (based on Entry time)
|
||||
if len(runner_ids) == 1:
|
||||
daily_rel_profits = [trade.rel_profit for trade in closed_trades if trade.rel_profit is not None]
|
||||
sns.lineplot(x=range(len(daily_rel_profits)), y=daily_rel_profits, ax=axs[2, 0])
|
||||
axs[2, 0].set_title('Daily Relative Profit')
|
||||
else:
|
||||
# Creating a DataFrame for the heatmap
|
||||
heatmap_data_list = []
|
||||
for trade in trades:
|
||||
if trade.status == TradeStatus.CLOSED:
|
||||
day = trade.entry_time.strftime('%m-%d') # Format date as 'MM-DD'
|
||||
#day = trade.entry_time.date()
|
||||
hour = trade.entry_time.hour
|
||||
profit = trade.profit
|
||||
heatmap_data_list.append({'Day': day, 'Hour': hour, 'Profit': profit})
|
||||
|
||||
heatmap_data = pd.DataFrame(heatmap_data_list)
|
||||
heatmap_data = heatmap_data.groupby(['Day', 'Hour']).sum().reset_index()
|
||||
heatmap_pivot = heatmap_data.pivot(index='Day', columns='Hour', values='Profit')
|
||||
|
||||
# Heatmap of Profits
|
||||
sns.heatmap(heatmap_pivot, cmap='viridis', ax=axs[2, 0])
|
||||
axs[2, 0].set_title('Heatmap of Profits (based on Entry time)')
|
||||
axs[2, 0].set_xlabel('Hour of Day')
|
||||
axs[2, 0].set_ylabel('Day')
|
||||
|
||||
# Plot 10: Profits Based on Hour of the Day (Entry)
|
||||
entry_hours = [trade.entry_time.hour for trade in closed_trades if trade.entry_time is not None]
|
||||
profits_by_hour = {}
|
||||
for hour, trade in zip(entry_hours, closed_trades):
|
||||
if hour not in profits_by_hour:
|
||||
profits_by_hour[hour] = 0
|
||||
profits_by_hour[hour] += trade.profit
|
||||
|
||||
# Sorting by hour for plotting
|
||||
sorted_hours = sorted(profits_by_hour.keys())
|
||||
sorted_profits = [profits_by_hour[hour] for hour in sorted_hours]
|
||||
|
||||
if sorted_profits:
|
||||
sns.barplot(x=sorted_hours, y=sorted_profits, ax=axs[2, 1])
|
||||
axs[2, 1].set_title('Profits by Hour of Day (Entry)')
|
||||
axs[2, 1].set_xlabel('Hour of Day')
|
||||
axs[2, 1].set_ylabel('Profit')
|
||||
else:
|
||||
# Handle the case where sorted_profits is empty
|
||||
axs[2, 1].text(0.5, 0.5, 'No data available',
|
||||
horizontalalignment='center',
|
||||
verticalalignment='center',
|
||||
transform=axs[2, 1].transAxes)
|
||||
axs[2, 1].set_title('Profits by Hour of Day (Entry)')
|
||||
|
||||
# Plot 11: Profits Based on Hour of the Day - based on Exit
|
||||
exit_hours = [trade.exit_time.hour for trade in closed_trades if trade.exit_time is not None]
|
||||
profits_by_hour = {}
|
||||
for hour, trade in zip(exit_hours, closed_trades):
|
||||
if hour not in profits_by_hour:
|
||||
profits_by_hour[hour] = 0
|
||||
profits_by_hour[hour] += trade.profit
|
||||
|
||||
# Sorting by hour for plotting
|
||||
sorted_hours = sorted(profits_by_hour.keys())
|
||||
sorted_profits = [profits_by_hour[hour] for hour in sorted_hours]
|
||||
|
||||
if sorted_profits:
|
||||
sns.barplot(x=sorted_hours, y=sorted_profits, ax=axs[2, 2])
|
||||
axs[2, 2].set_title('Profits by Hour of Day (Exit)')
|
||||
axs[2, 2].set_xlabel('Hour of Day')
|
||||
axs[2, 2].set_ylabel('Profit')
|
||||
else:
|
||||
# Handle the case where sorted_profits is empty
|
||||
axs[2, 2].text(0.5, 0.5, 'No data available',
|
||||
horizontalalignment='center',
|
||||
verticalalignment='center',
|
||||
transform=axs[2, 2].transAxes)
|
||||
axs[2, 2].set_title('Profits by Hour of Day (Exit)')
|
||||
|
||||
# Plot 12: Calculate profits by day of the week
|
||||
day_of_week_profits = {i: 0 for i in range(7)} # Dictionary to store profits for each day of the week
|
||||
|
||||
for trade in trades:
|
||||
if trade.status == TradeStatus.CLOSED:
|
||||
day_of_week = trade.exit_time.weekday() # Monday is 0 and Sunday is 6
|
||||
day_of_week_profits[day_of_week] += trade.profit
|
||||
|
||||
days = ['Mo', 'Tue', 'Wed', 'Thu', 'Fri']
|
||||
# Additional Plot: Strategy Performance by Day of the Week
|
||||
axs[2, 3].bar(days, [day_of_week_profits[i] for i in range(5)])
|
||||
axs[2, 3].set_title('Profit by Day of the Week')
|
||||
axs[2, 3].set_xlabel('Day of the Week')
|
||||
axs[2, 3].set_ylabel('Cumulative Profit')
|
||||
|
||||
#filename
|
||||
file = batch_id if batch_id is not None else runner_ids[0]
|
||||
image_file_name = f"{file}.png"
|
||||
image_path = str(MEDIA_DIRECTORY / "basic" / image_file_name)
|
||||
|
||||
# Adjust layout and save the combined plot as an image
|
||||
plt.tight_layout()
|
||||
|
||||
if stream is False:
|
||||
plt.savefig(image_path)
|
||||
plt.close()
|
||||
return 0, None
|
||||
else:
|
||||
# Return the image as a BytesIO stream
|
||||
img_stream = BytesIO()
|
||||
plt.savefig(img_stream, format='png')
|
||||
plt.close()
|
||||
img_stream.seek(0) # Rewind the stream to the beginning
|
||||
return 0, img_stream
|
||||
|
||||
##Generates BATCH REPORT again for the given batch_id
|
||||
##USAGE: python createbatchimage.py <batch_id>
|
||||
#Parse the command-line arguments
|
||||
parser = argparse.ArgumentParser(description="Generate trading report image with batch ID")
|
||||
parser.add_argument("batch_id", type=str, help="The batch ID for the report")
|
||||
args = parser.parse_args()
|
||||
#parser = argparse.ArgumentParser(description="Generate trading report image with batch ID")
|
||||
# parser.add_argument("server", type=str, help="The server IP for the report")
|
||||
# parser.add_argument("batch_id", type=str, help="The batch ID for the report")
|
||||
# args = parser.parse_args()
|
||||
|
||||
batch_id = args.batch_id
|
||||
# batch_id = args.batch_id
|
||||
# server = args.server
|
||||
|
||||
# Generate the report image
|
||||
res, val = mt.generate_trading_report_image(batch_id=batch_id)
|
||||
# Generate the report image, using local copy, which will replace the current e0639b45 4bea3a54
|
||||
res, val = generate_trading_report_image(batch_id="4bea3a54", server="142.132.188.109")
|
||||
|
||||
# Print the result
|
||||
if res == 0:
|
||||
print("BATCH REPORT CREATED")
|
||||
else:
|
||||
print(f"BATCH REPORT ERROR - {val}")
|
||||
|
||||
|
||||
|
||||
@ -51,6 +51,9 @@ def convert_historical_bars(daily_bars):
|
||||
for i in range(len(daily_bars)):
|
||||
bar = daily_bars[i]
|
||||
|
||||
if bar is None:
|
||||
continue
|
||||
|
||||
# Calculate the HLCC4 indicator
|
||||
hlcc4 = (bar['h'] + bar['l'] + bar['c'] + bar['o']) / 4
|
||||
datum = to_datetime(bar['t'], utc=True)
|
||||
|
||||
Reference in New Issue
Block a user