bugfix + optimizedcutoffs button on gui
This commit is contained in:
David Brazda
@ -37,7 +37,7 @@ from v2realbot.strategyblocks.indicators.indicators_hub import populate_dynamic_
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from v2realbot.interfaces.backtest_interface import BacktestInterface
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import os
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from v2realbot.reporting.metricstoolsimage import generate_trading_report_image
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import gc
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#import gc
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#from pyinstrument import Profiler
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#adding lock to ensure thread safety of TinyDB (in future will be migrated to proper db)
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lock = Lock()
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@ -524,7 +524,7 @@ def batch_run_manager(id: UUID, runReq: RunRequest, rundays: list[RunDay]):
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except Exception as e:
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print("Nepodarilo se vytvorit report image", str(e)+format_exc())
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gc.collect()
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#gc.collect()
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#stratin run
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def run_stratin(id: UUID, runReq: RunRequest, synchronous: bool = False, inter_batch_params: dict = None):
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@ -22,7 +22,8 @@ from rich import print
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import queue
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from alpaca.trading.models import Calendar
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from tqdm import tqdm
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import time
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from traceback import format_exc
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"""
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Trade offline data streamer, based on Alpaca historical data.
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"""
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@ -101,6 +102,19 @@ class Trade_Offline_Streamer(Thread):
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#REFACTOR STARTS HERE
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#print(f"{self.time_from=} {self.time_to=}")
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def get_calendar_with_retry(self, calendar_request, max_retries=3, delay=4):
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attempts = 0
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while attempts < max_retries:
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try:
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cal_dates = self.clientTrading.get_calendar(calendar_request)
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return cal_dates
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except Exception as e:
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attempts += 1
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if attempts >= max_retries:
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raise
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print(f"Attempt {attempts}: Error occurred - {e}. Retrying in {delay} seconds...")
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time.sleep(delay)
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if OFFLINE_MODE:
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#just one day - same like time_from
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den = str(self.time_to.date())
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@ -108,8 +122,15 @@ class Trade_Offline_Streamer(Thread):
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cal_dates = [bt_day]
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else:
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calendar_request = GetCalendarRequest(start=self.time_from,end=self.time_to)
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#toto zatim workaround - dat do retry funkce a obecne vymyslet exception handling, abych byl notifikovan a bylo videt okamzite v logu a na frontendu
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try:
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cal_dates = self.clientTrading.get_calendar(calendar_request)
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#ic(cal_dates)
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except Exception as e:
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print("CHYBA - retrying in 4s: " + str(e) + format_exc())
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time.sleep(5)
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cal_dates = self.clientTrading.get_calendar(calendar_request)
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#zatim podpora pouze main session
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#zatim podpora pouze 1 symbolu, predelat na froloop vsech symbolu ze symbpole
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@ -33,8 +33,9 @@ from time import sleep
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import v2realbot.reporting.metricstools as mt
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from v2realbot.reporting.metricstoolsimage import generate_trading_report_image
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from traceback import format_exc
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from v2realbot.reporting.optimizecutoffs import find_optimal_cutoff
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#from async io import Queue, QueueEmpty
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#
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# install()
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# ic.configureOutput(includeContext=True)
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# def threadName():
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@ -561,6 +562,20 @@ def _generate_report_image(runner_ids: list[UUID]):
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except Exception as e:
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raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error: {str(e)}" + format_exc())
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#TODO toto bude zaklad pro obecnou funkci, ktera bude volat ruzne analyzy
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#vstupem bude obecny objekt, ktery ponese nazev analyzy + atributy
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@app.post("/batches/optimizecutoff/{batch_id}", dependencies=[Depends(api_key_auth)], responses={200: {"content": {"image/png": {}}}})
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def _generate_analysis(batch_id: str):
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try:
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res, stream = find_optimal_cutoff(batch_id=batch_id, steps=50, stream=True)
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if res == 0: return StreamingResponse(stream, media_type="image/png",headers={"Content-Disposition": "attachment; filename=optimizedcutoff.png"})
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elif res < 0:
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raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error: {res}:{id}")
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except Exception as e:
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raise HTTPException(status_code=status.HTTP_406_NOT_ACCEPTABLE, detail=f"Error: {str(e)}" + format_exc())
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#TestList APIS - do budoucna predelat SQL do separatnich funkci
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@app.post('/testlists/', dependencies=[Depends(api_key_auth)])
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def create_record(testlist: TestList):
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166
v2realbot/reporting/archive/optimizecutoff.py
Normal file
166
v2realbot/reporting/archive/optimizecutoff.py
Normal file
@ -0,0 +1,166 @@
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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#, 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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# Assuming Trade, TradeStatus, TradeDirection, TradeStoplossType classes are defined elsewhere
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#TODO:
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#OPTIMALIZOVAT TENTO nebo DRUHY soubor
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#Vyzkouset v realu
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#ZKUSIT NA RELATIVNI PROFIT TAKE
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#ZAPRACOVAT DO GRAFU nebo do nejakeho toolu
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def find_optimal_cutoff(runner_ids: list = None, batch_id: str = None, stream: bool = False):
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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 =cs.get_archived_runnerslist_byBatchID(batch_id)
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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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cnt_max = len(runner_ids)
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cnt = 0
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#zatim zjistujeme start a end z min a max dni - jelikoz muze byt i seznam runner_ids a nejenom batch
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end_date = None
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start_date = None
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for id in runner_ids:
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cnt += 1
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#get runner
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res, sada =cs.get_archived_runner_header_byID(id)
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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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print("archrunner")
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#print(sada)
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if cnt == 1:
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start_date = sada.bt_from if sada.mode in [Mode.BT,Mode.PREP] else sada.started
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if cnt == cnt_max:
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end_date = sada.bt_to if sada.mode in [Mode.BT or Mode.PREP] else sada.stopped
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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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# symbol = sada.symbol
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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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print(closed_trades)
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if len(closed_trades) == 0:
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return -1, "image generation no closed trades"
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# Group trades by date and calculate daily profits
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trades_by_day = defaultdict(list)
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for trade in trades:
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if trade.status == TradeStatus.CLOSED and trade.exit_time:
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trade_day = trade.exit_time.date()
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trades_by_day[trade_day].append(trade)
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# Define a range of potential cutoff values
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# min_profit = min(trade.profit for trade in trades if trade.profit is not None)
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# max_profit = max(trade.profit for trade in trades if trade.profit is not None)
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min_profit = 0
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max_profit = 1000
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potential_cutoffs = np.linspace(min_profit, max_profit, 100)
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# Function to calculate total profit for a given cutoff
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def calculate_total_profit(cutoff):
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total_profit = 0
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for day, day_trades in trades_by_day.items():
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daily_profit = 0
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for trade in day_trades:
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daily_profit += trade.profit
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if daily_profit >= cutoff: # Stop if daily profit reaches the cutoff
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break
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total_profit += min(daily_profit, cutoff) # Add the minimum of daily profit or cutoff
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return total_profit
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# Evaluate each cutoff
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cutoff_profits = {cutoff: calculate_total_profit(cutoff) for cutoff in potential_cutoffs}
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# Find the optimal cutoff
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optimal_cutoff = max(cutoff_profits, key=cutoff_profits.get)
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max_profit = cutoff_profits[optimal_cutoff]
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# Plotting
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plt.figure(figsize=(10, 6))
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plt.plot(list(cutoff_profits.keys()), list(cutoff_profits.values()), label='Total Profit')
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plt.scatter(optimal_cutoff, max_profit, color='red', label='Optimal Cutoff')
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plt.title('Optimal Intra-Day Profit Cutoff Analysis')
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plt.xlabel('Profit Cutoff')
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plt.ylabel('Total Profit')
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plt.legend()
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plt.grid(True)
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plt.savefig('optimal_cutoff.png')
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return optimal_cutoff, max_profit
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# Example usage
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# trades = [list of Trade objects]
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if __name__ == '__main__':
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# id_list = ["e8938b2e-8462-441a-8a82-d823c6a025cb"]
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# generate_trading_report_image(runner_ids=id_list)
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batch_id = "90973e57"
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optimal_cutoff, max_profit = find_optimal_cutoff(batch_id=batch_id)
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print(f"Optimal Cutoff: {optimal_cutoff}, Max Profit: {max_profit}")
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167
v2realbot/reporting/archive/optimizecutoffprofloss.py
Normal file
167
v2realbot/reporting/archive/optimizecutoffprofloss.py
Normal file
@ -0,0 +1,167 @@
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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#, 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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# Assuming Trade, TradeStatus, TradeDirection, TradeStoplossType classes are defined elsewhere
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#LOSS and PROFIT without GRAPH
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def find_optimal_cutoff(runner_ids: list = None, batch_id: str = None, stream: bool = False):
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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 =cs.get_archived_runnerslist_byBatchID(batch_id)
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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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cnt_max = len(runner_ids)
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cnt = 0
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#zatim zjistujeme start a end z min a max dni - jelikoz muze byt i seznam runner_ids a nejenom batch
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end_date = None
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start_date = None
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for id in runner_ids:
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cnt += 1
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#get runner
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res, sada =cs.get_archived_runner_header_byID(id)
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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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print("archrunner")
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#print(sada)
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if cnt == 1:
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start_date = sada.bt_from if sada.mode in [Mode.BT,Mode.PREP] else sada.started
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if cnt == cnt_max:
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end_date = sada.bt_to if sada.mode in [Mode.BT or Mode.PREP] else sada.stopped
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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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# symbol = sada.symbol
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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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#print(closed_trades)
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if len(closed_trades) == 0:
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return -1, "image generation no closed trades"
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# Group trades by date and calculate daily profits
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trades_by_day = defaultdict(list)
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for trade in trades:
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if trade.status == TradeStatus.CLOSED and trade.exit_time:
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trade_day = trade.exit_time.date()
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trades_by_day[trade_day].append(trade)
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# Define ranges for loss and profit cutoffs
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min_profit = 0
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max_profit = 1000
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profit_cutoffs = np.linspace(min_profit, max_profit, 20)
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min_loss = 0
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max_loss = -1000
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loss_cutoffs = np.linspace(min_loss, max_loss, 20)
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#print(profit_cutoffs, loss_cutoffs)
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# Function to calculate total profit for a given cutoff
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def calculate_total_profit(profit_cutoff, loss_cutoff):
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total_profit = 0
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for day, day_trades in trades_by_day.items():
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daily_profit = 0
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for trade in day_trades:
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daily_profit += trade.profit
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if daily_profit >= profit_cutoff or daily_profit <= loss_cutoff:
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break
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total_profit += daily_profit
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return total_profit
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# Initialize variables to store optimal cutoffs and max total profit
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optimal_profit_cutoff = max_profit
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optimal_loss_cutoff = min_loss
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max_total_profit = float('-inf')
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# Initialize a matrix to store total profits for each combination of cutoffs
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total_profits_matrix = np.zeros((len(profit_cutoffs), len(loss_cutoffs)))
|
||||
|
||||
for i, profit_cutoff in enumerate(profit_cutoffs):
|
||||
for j, loss_cutoff in enumerate(loss_cutoffs):
|
||||
total_profit = calculate_total_profit(profit_cutoff, loss_cutoff)
|
||||
total_profits_matrix[i, j] = total_profit
|
||||
if total_profit > max_total_profit:
|
||||
max_total_profit = total_profit
|
||||
optimal_profit_cutoff = profit_cutoff
|
||||
optimal_loss_cutoff = loss_cutoff
|
||||
|
||||
# Plotting the results as a heatmap
|
||||
plt.figure(figsize=(20, 15))
|
||||
sns.heatmap(total_profits_matrix, xticklabels=np.round(loss_cutoffs, 2), yticklabels=np.round(profit_cutoffs, 2),
|
||||
annot=True, fmt=".0f", cmap="viridis") #fmt=".0f",
|
||||
#plasma, coolmap, inferno, viridis
|
||||
plt.title("Total Profit for Combinations of Profit and Loss Cutoffs")
|
||||
plt.xlabel("Loss Cutoff")
|
||||
plt.ylabel("Profit Cutoff")
|
||||
plt.savefig('optimal_cutoff.png')
|
||||
|
||||
return optimal_profit_cutoff, optimal_loss_cutoff, max_total_profit
|
||||
|
||||
# Example usage
|
||||
# trades = [list of Trade objects]
|
||||
if __name__ == '__main__':
|
||||
# id_list = ["e8938b2e-8462-441a-8a82-d823c6a025cb"]
|
||||
# generate_trading_report_image(runner_ids=id_list)
|
||||
batch_id = "c76b4414"
|
||||
optimal_profit_cutoff, optimal_loss_cutoff, max_profit = find_optimal_cutoff(batch_id=batch_id)
|
||||
print(f"Optimal Profit Cutoff: {optimal_profit_cutoff}, Optimal Loss Cutoff: {optimal_loss_cutoff}, Max Profit: {max_profit}")
|
||||
169
v2realbot/reporting/archive/optimizecutoffv2.py
Normal file
169
v2realbot/reporting/archive/optimizecutoffv2.py
Normal file
@ -0,0 +1,169 @@
|
||||
import matplotlib
|
||||
import matplotlib.dates as mdates
|
||||
#matplotlib.use('Agg') # Set the Matplotlib backend to 'Agg'
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from typing import List
|
||||
from enum import Enum
|
||||
import numpy as np
|
||||
import v2realbot.controller.services as cs
|
||||
from rich import print
|
||||
from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
|
||||
from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
|
||||
from pathlib import Path
|
||||
from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
|
||||
from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
|
||||
from io import BytesIO
|
||||
from v2realbot.utils.historicals import get_historical_bars
|
||||
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
|
||||
from collections import defaultdict
|
||||
# Assuming Trade, TradeStatus, TradeDirection, TradeStoplossType classes are defined elsewhere
|
||||
|
||||
#LOSS and PROFIT without GRAPH
|
||||
def find_optimal_cutoff(runner_ids: list = None, batch_id: str = None, stream: bool = False):
|
||||
|
||||
#TODO dopracovat drawdown a minimalni a maximalni profity nikoliv cumulovane, zamyslet se
|
||||
#TODO list of runner_ids
|
||||
#TODO pridelat na vytvoreni runnera a batche, samostatne REST API + na remove archrunnera
|
||||
|
||||
if runner_ids is None and batch_id is None:
|
||||
return -2, f"runner_id or batch_id must be present"
|
||||
|
||||
if batch_id is not None:
|
||||
res, runner_ids =cs.get_archived_runnerslist_byBatchID(batch_id)
|
||||
|
||||
if res != 0:
|
||||
print(f"no batch {batch_id} found")
|
||||
return -1, f"no batch {batch_id} found"
|
||||
|
||||
trades = []
|
||||
cnt_max = len(runner_ids)
|
||||
cnt = 0
|
||||
#zatim zjistujeme start a end z min a max dni - jelikoz muze byt i seznam runner_ids a nejenom batch
|
||||
end_date = None
|
||||
start_date = None
|
||||
for id in runner_ids:
|
||||
cnt += 1
|
||||
#get runner
|
||||
res, sada =cs.get_archived_runner_header_byID(id)
|
||||
if res != 0:
|
||||
print(f"no runner {id} found")
|
||||
return -1, f"no runner {id} found"
|
||||
|
||||
print("archrunner")
|
||||
#print(sada)
|
||||
|
||||
if cnt == 1:
|
||||
start_date = sada.bt_from if sada.mode in [Mode.BT,Mode.PREP] else sada.started
|
||||
if cnt == cnt_max:
|
||||
end_date = sada.bt_to if sada.mode in [Mode.BT or Mode.PREP] else sada.stopped
|
||||
# Parse trades
|
||||
|
||||
trades_dicts = sada.metrics["prescr_trades"]
|
||||
|
||||
for trade_dict in trades_dicts:
|
||||
trade_dict['last_update'] = datetime.fromtimestamp(trade_dict.get('last_update')).astimezone(zoneNY) if trade_dict['last_update'] is not None else None
|
||||
trade_dict['entry_time'] = datetime.fromtimestamp(trade_dict.get('entry_time')).astimezone(zoneNY) if trade_dict['entry_time'] is not None else None
|
||||
trade_dict['exit_time'] = datetime.fromtimestamp(trade_dict.get('exit_time')).astimezone(zoneNY) if trade_dict['exit_time'] is not None else None
|
||||
trades.append(Trade(**trade_dict))
|
||||
|
||||
#print(trades)
|
||||
|
||||
# symbol = sada.symbol
|
||||
# #hour bars for backtested period
|
||||
# print(start_date,end_date)
|
||||
# bars= get_historical_bars(symbol, start_date, end_date, TimeFrame.Hour)
|
||||
# print("bars for given period",bars)
|
||||
# """Bars a dictionary with the following keys:
|
||||
# * high: A list of high prices
|
||||
# * low: A list of low prices
|
||||
# * volume: A list of volumes
|
||||
# * close: A list of close prices
|
||||
# * hlcc4: A list of HLCC4 indicators
|
||||
# * open: A list of open prices
|
||||
# * time: A list of times in UTC (ISO 8601 format)
|
||||
# * trades: A list of number of trades
|
||||
# * resolution: A list of resolutions (all set to 'D')
|
||||
# * confirmed: A list of booleans (all set to True)
|
||||
# * vwap: A list of VWAP indicator
|
||||
# * updated: A list of booleans (all set to True)
|
||||
# * index: A list of integers (from 0 to the length of the list of daily bars)
|
||||
# """
|
||||
|
||||
# Filter to only use trades with status 'CLOSED'
|
||||
closed_trades = [trade for trade in trades if trade.status == TradeStatus.CLOSED]
|
||||
|
||||
print(closed_trades)
|
||||
|
||||
if len(closed_trades) == 0:
|
||||
return -1, "image generation no closed trades"
|
||||
|
||||
# Group trades by date and calculate daily profits
|
||||
trades_by_day = defaultdict(list)
|
||||
for trade in trades:
|
||||
if trade.status == TradeStatus.CLOSED and trade.exit_time:
|
||||
trade_day = trade.exit_time.date()
|
||||
trades_by_day[trade_day].append(trade)
|
||||
|
||||
# Define ranges for loss and profit cutoffs
|
||||
min_profit = 50
|
||||
max_profit = 700 # Set an upper bound based on your data
|
||||
profit_cutoffs = np.linspace(min_profit, max_profit, 50) # Adjust number of points as needed
|
||||
|
||||
min_loss = -50
|
||||
max_loss = -700 # Assuming losses are negative values
|
||||
loss_cutoffs = np.linspace(min_loss, max_loss, 50)
|
||||
|
||||
def calculate_total_profit(profit_cutoff, loss_cutoff):
|
||||
total_profit = 0
|
||||
for day, day_trades in trades_by_day.items():
|
||||
daily_profit = 0
|
||||
for trade in day_trades:
|
||||
daily_profit += trade.profit
|
||||
if daily_profit >= profit_cutoff or daily_profit <= loss_cutoff:
|
||||
break
|
||||
total_profit += daily_profit
|
||||
return total_profit
|
||||
|
||||
# Evaluate each combination of cutoffs
|
||||
optimal_profit_cutoff = max_profit
|
||||
optimal_loss_cutoff = min_loss
|
||||
max_total_profit = float('-inf')
|
||||
|
||||
for profit_cutoff in profit_cutoffs:
|
||||
for loss_cutoff in loss_cutoffs:
|
||||
total_profit = calculate_total_profit(profit_cutoff, loss_cutoff)
|
||||
if total_profit > max_total_profit:
|
||||
max_total_profit = total_profit
|
||||
optimal_profit_cutoff = profit_cutoff
|
||||
optimal_loss_cutoff = loss_cutoff
|
||||
|
||||
print(f"Optimal Profit Cutoff: {optimal_profit_cutoff}, Optimal Loss Cutoff: {optimal_loss_cutoff}, Max Profit: {max_total_profit}")
|
||||
|
||||
# Optional: Plot the results or return them for further analysis
|
||||
|
||||
return optimal_profit_cutoff, optimal_loss_cutoff, max_total_profit
|
||||
|
||||
# # Plotting
|
||||
# plt.figure(figsize=(10, 6))
|
||||
# plt.plot(list(cutoff_profits.keys()), list(cutoff_profits.values()), label='Total Profit')
|
||||
# plt.scatter(optimal_cutoff, max_profit, color='red', label='Optimal Cutoff')
|
||||
# plt.title('Optimal Intra-Day Profit Cutoff Analysis')
|
||||
# plt.xlabel('Profit Cutoff')
|
||||
# plt.ylabel('Total Profit')
|
||||
# plt.legend()
|
||||
# plt.grid(True)
|
||||
# plt.savefig('optimal_cutoff.png')
|
||||
|
||||
# return optimal_cutoff, max_profit
|
||||
|
||||
# Example usage
|
||||
# trades = [list of Trade objects]
|
||||
if __name__ == '__main__':
|
||||
# id_list = ["e8938b2e-8462-441a-8a82-d823c6a025cb"]
|
||||
# generate_trading_report_image(runner_ids=id_list)
|
||||
batch_id = "c76b4414"
|
||||
optimal_profit_cutoff, optimal_loss_cutoff, max_profit = find_optimal_cutoff(batch_id=batch_id)
|
||||
print(f"Optimal Profit Cutoff: {optimal_profit_cutoff}, Optimal Loss Cutoff: {optimal_loss_cutoff}, Max Profit: {max_profit}")
|
||||
241
v2realbot/reporting/optimizecutoffs.py
Normal file
241
v2realbot/reporting/optimizecutoffs.py
Normal file
@ -0,0 +1,241 @@
|
||||
import matplotlib
|
||||
import matplotlib.dates as mdates
|
||||
#matplotlib.use('Agg') # Set the Matplotlib backend to 'Agg'
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from typing import List
|
||||
from enum import Enum
|
||||
import numpy as np
|
||||
import v2realbot.controller.services as cs
|
||||
from rich import print
|
||||
from v2realbot.common.PrescribedTradeModel import TradeDirection, TradeStatus, Trade, TradeStoplossType
|
||||
from v2realbot.utils.utils import isrising, isfalling,zoneNY, price2dec, safe_get#, print
|
||||
from pathlib import Path
|
||||
from v2realbot.config import WEB_API_KEY, DATA_DIR, MEDIA_DIRECTORY
|
||||
from v2realbot.enums.enums import RecordType, StartBarAlign, Mode, Account, OrderSide
|
||||
from io import BytesIO
|
||||
from v2realbot.utils.historicals import get_historical_bars
|
||||
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
|
||||
from collections import defaultdict
|
||||
from scipy.stats import zscore
|
||||
from io import BytesIO
|
||||
# Assuming Trade, TradeStatus, TradeDirection, TradeStoplossType classes are defined elsewhere
|
||||
|
||||
#LOSS and PROFIT without GRAPH
|
||||
def find_optimal_cutoff(runner_ids: list = None, batch_id: str = None, stream: bool = False, rem_outliers:bool = False, file: str = "optimalcutoff.png",steps:int = 50):
|
||||
|
||||
#TODO dopracovat drawdown a minimalni a maximalni profity nikoliv cumulovane, zamyslet se
|
||||
#TODO list of runner_ids
|
||||
#TODO pridelat na vytvoreni runnera a batche, samostatne REST API + na remove archrunnera
|
||||
|
||||
if runner_ids is None and batch_id is None:
|
||||
return -2, f"runner_id or batch_id must be present"
|
||||
|
||||
if batch_id is not None:
|
||||
res, runner_ids =cs.get_archived_runnerslist_byBatchID(batch_id)
|
||||
|
||||
if res != 0:
|
||||
print(f"no batch {batch_id} found")
|
||||
return -1, f"no batch {batch_id} found"
|
||||
|
||||
trades = []
|
||||
cnt_max = len(runner_ids)
|
||||
cnt = 0
|
||||
#zatim zjistujeme start a end z min a max dni - jelikoz muze byt i seznam runner_ids a nejenom batch
|
||||
end_date = None
|
||||
start_date = None
|
||||
for id in runner_ids:
|
||||
cnt += 1
|
||||
#get runner
|
||||
res, sada =cs.get_archived_runner_header_byID(id)
|
||||
if res != 0:
|
||||
print(f"no runner {id} found")
|
||||
return -1, f"no runner {id} found"
|
||||
|
||||
#print("archrunner")
|
||||
#print(sada)
|
||||
|
||||
if cnt == 1:
|
||||
start_date = sada.bt_from if sada.mode in [Mode.BT,Mode.PREP] else sada.started
|
||||
if cnt == cnt_max:
|
||||
end_date = sada.bt_to if sada.mode in [Mode.BT or Mode.PREP] else sada.stopped
|
||||
# Parse trades
|
||||
|
||||
trades_dicts = sada.metrics["prescr_trades"]
|
||||
|
||||
for trade_dict in trades_dicts:
|
||||
trade_dict['last_update'] = datetime.fromtimestamp(trade_dict.get('last_update')).astimezone(zoneNY) if trade_dict['last_update'] is not None else None
|
||||
trade_dict['entry_time'] = datetime.fromtimestamp(trade_dict.get('entry_time')).astimezone(zoneNY) if trade_dict['entry_time'] is not None else None
|
||||
trade_dict['exit_time'] = datetime.fromtimestamp(trade_dict.get('exit_time')).astimezone(zoneNY) if trade_dict['exit_time'] is not None else None
|
||||
trades.append(Trade(**trade_dict))
|
||||
|
||||
#print(trades)
|
||||
|
||||
# symbol = sada.symbol
|
||||
# #hour bars for backtested period
|
||||
# print(start_date,end_date)
|
||||
# bars= get_historical_bars(symbol, start_date, end_date, TimeFrame.Hour)
|
||||
# print("bars for given period",bars)
|
||||
# """Bars a dictionary with the following keys:
|
||||
# * high: A list of high prices
|
||||
# * low: A list of low prices
|
||||
# * volume: A list of volumes
|
||||
# * close: A list of close prices
|
||||
# * hlcc4: A list of HLCC4 indicators
|
||||
# * open: A list of open prices
|
||||
# * time: A list of times in UTC (ISO 8601 format)
|
||||
# * trades: A list of number of trades
|
||||
# * resolution: A list of resolutions (all set to 'D')
|
||||
# * confirmed: A list of booleans (all set to True)
|
||||
# * vwap: A list of VWAP indicator
|
||||
# * updated: A list of booleans (all set to True)
|
||||
# * index: A list of integers (from 0 to the length of the list of daily bars)
|
||||
# """
|
||||
|
||||
# Filter to only use trades with status 'CLOSED'
|
||||
closed_trades = [trade for trade in trades if trade.status == TradeStatus.CLOSED]
|
||||
|
||||
#print(closed_trades)
|
||||
|
||||
if len(closed_trades) == 0:
|
||||
return -1, "image generation no closed trades"
|
||||
|
||||
# # Group trades by date and calculate daily profits
|
||||
# trades_by_day = defaultdict(list)
|
||||
# for trade in trades:
|
||||
# if trade.status == TradeStatus.CLOSED and trade.exit_time:
|
||||
# trade_day = trade.exit_time.date()
|
||||
# trades_by_day[trade_day].append(trade)
|
||||
|
||||
# Precompute daily cumulative profits
|
||||
daily_cumulative_profits = defaultdict(list)
|
||||
for trade in trades:
|
||||
if trade.status == TradeStatus.CLOSED and trade.exit_time:
|
||||
day = trade.exit_time.date()
|
||||
daily_cumulative_profits[day].append(trade.profit)
|
||||
|
||||
for day in daily_cumulative_profits:
|
||||
daily_cumulative_profits[day] = np.cumsum(daily_cumulative_profits[day])
|
||||
|
||||
|
||||
if rem_outliers:
|
||||
# Remove outliers based on z-scores
|
||||
def remove_outliers(cumulative_profits):
|
||||
all_profits = [profit[-1] for profit in cumulative_profits.values() if len(profit) > 0]
|
||||
z_scores = zscore(all_profits)
|
||||
print(z_scores)
|
||||
filtered_profits = {}
|
||||
for day, profits in cumulative_profits.items():
|
||||
if len(profits) > 0:
|
||||
day_z_score = z_scores[list(cumulative_profits.keys()).index(day)]
|
||||
if abs(day_z_score) < 3: # Adjust threshold as needed
|
||||
filtered_profits[day] = profits
|
||||
return filtered_profits
|
||||
|
||||
daily_cumulative_profits = remove_outliers(daily_cumulative_profits)
|
||||
|
||||
|
||||
# OPT1 Dynamically calculate profit_range and loss_range - based on eod daily profit
|
||||
# all_final_profits = [profits[-1] for profits in daily_cumulative_profits.values() if len(profits) > 0]
|
||||
# max_profit = max(all_final_profits)
|
||||
# min_profit = min(all_final_profits)
|
||||
# profit_range = (0, max_profit) if max_profit > 0 else (0, 0)
|
||||
# loss_range = (min_profit, 0) if min_profit < 0 else (0, 0)
|
||||
|
||||
# OPT2 Calculate profit_range and loss_range based on all cumulative profits
|
||||
all_cumulative_profits = np.concatenate([profits for profits in daily_cumulative_profits.values()])
|
||||
max_cumulative_profit = np.max(all_cumulative_profits)
|
||||
min_cumulative_profit = np.min(all_cumulative_profits)
|
||||
profit_range = (0, max_cumulative_profit) if max_cumulative_profit > 0 else (0, 0)
|
||||
loss_range = (min_cumulative_profit, 0) if min_cumulative_profit < 0 else (0, 0)
|
||||
|
||||
print("Calculated ranges", profit_range, loss_range)
|
||||
|
||||
num_points = steps # Adjust for speed vs accuracy
|
||||
profit_cutoffs = np.linspace(*profit_range, num_points)
|
||||
loss_cutoffs = np.linspace(*loss_range, num_points)
|
||||
|
||||
# OPT 3Statically define ranges for loss and profit cutoffs
|
||||
# profit_range = (0, 1000) # Adjust based on your data
|
||||
# loss_range = (-1000, 0)
|
||||
# num_points = 20 # Adjust for speed vs accuracy
|
||||
|
||||
profit_cutoffs = np.linspace(*profit_range, num_points)
|
||||
loss_cutoffs = np.linspace(*loss_range, num_points)
|
||||
|
||||
total_profits_matrix = np.zeros((len(profit_cutoffs), len(loss_cutoffs)))
|
||||
|
||||
for i, profit_cutoff in enumerate(profit_cutoffs):
|
||||
for j, loss_cutoff in enumerate(loss_cutoffs):
|
||||
total_profit = 0
|
||||
for daily_profit in daily_cumulative_profits.values():
|
||||
cutoff_index = np.where((daily_profit >= profit_cutoff) | (daily_profit <= loss_cutoff))[0]
|
||||
if cutoff_index.size > 0:
|
||||
total_profit += daily_profit[cutoff_index[0]]
|
||||
else:
|
||||
total_profit += daily_profit[-1] if daily_profit.size > 0 else 0
|
||||
total_profits_matrix[i, j] = total_profit
|
||||
|
||||
# Find the optimal combination
|
||||
optimal_idx = np.unravel_index(total_profits_matrix.argmax(), total_profits_matrix.shape)
|
||||
optimal_profit_cutoff = profit_cutoffs[optimal_idx[0]]
|
||||
optimal_loss_cutoff = loss_cutoffs[optimal_idx[1]]
|
||||
max_profit = total_profits_matrix[optimal_idx]
|
||||
|
||||
# Plotting
|
||||
# Setting up dark mode for the plots
|
||||
plt.style.use('dark_background')
|
||||
|
||||
# Optionally, you can further customize colors, labels, and axes
|
||||
params = {
|
||||
'axes.titlesize': 9,
|
||||
'axes.labelsize': 8,
|
||||
'xtick.labelsize': 9,
|
||||
'ytick.labelsize': 9,
|
||||
'axes.labelcolor': '#a9a9a9', #a1a3aa',
|
||||
'axes.facecolor': '#121722', #'#0e0e0e', #202020', # Dark background for plot area
|
||||
'axes.grid': False, # Turn off the grid globally
|
||||
'grid.color': 'gray', # If the grid is on, set grid line color
|
||||
'grid.linestyle': '--', # Grid line style
|
||||
'grid.linewidth': 1,
|
||||
'xtick.color': '#a9a9a9',
|
||||
'ytick.color': '#a9a9a9',
|
||||
'axes.edgecolor': '#a9a9a9'
|
||||
}
|
||||
plt.rcParams.update(params)
|
||||
plt.figure(figsize=(10, 8))
|
||||
sns.heatmap(total_profits_matrix, xticklabels=np.rint(loss_cutoffs).astype(int), yticklabels=np.rint(profit_cutoffs).astype(int), cmap="viridis")
|
||||
plt.xticks(rotation=90) # Rotate x-axis labels to be vertical
|
||||
plt.yticks(rotation=0) # Keep y-axis labels horizontal
|
||||
plt.gca().invert_yaxis()
|
||||
plt.gca().invert_xaxis()
|
||||
plt.suptitle("Total Profit for Combinations of Profit and Loss Cutoffs", fontsize=16)
|
||||
plt.title(f"Optimal Profit Cutoff: {optimal_profit_cutoff:.2f}, Optimal Loss Cutoff: {optimal_loss_cutoff:.2f}, Max Profit: {max_profit:.2f}", fontsize=10)
|
||||
plt.xlabel("Loss Cutoff")
|
||||
plt.ylabel("Profit Cutoff")
|
||||
|
||||
if stream is False:
|
||||
plt.savefig(file)
|
||||
plt.close()
|
||||
print(f"Optimal Profit Cutoff(rem_outliers:{rem_outliers}): {optimal_profit_cutoff}, Optimal Loss Cutoff: {optimal_loss_cutoff}, Max Profit: {max_profit}")
|
||||
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
|
||||
|
||||
# Example usage
|
||||
# trades = [list of Trade objects]
|
||||
if __name__ == '__main__':
|
||||
# id_list = ["e8938b2e-8462-441a-8a82-d823c6a025cb"]
|
||||
# generate_trading_report_image(runner_ids=id_list)
|
||||
batch_id = "c76b4414"
|
||||
res, val = find_optimal_cutoff(batch_id=batch_id, file="optimal_cutoff_vectorized.png",steps=20)
|
||||
#res, val = find_optimal_cutoff(batch_id=batch_id, rem_outliers=True, file="optimal_cutoff_vectorized_nooutliers.png")
|
||||
|
||||
print(res,val)
|
||||
@ -310,6 +310,7 @@
|
||||
<button id="button_export_xml" class="btn btn-outline-success btn-sm">Export xml</button>
|
||||
<button id="button_export_csv" class="btn btn-outline-success btn-sm">Export csv</button>
|
||||
<button id="button_report" class="btn btn-outline-success btn-sm">Report(q)</button>
|
||||
<button id="button_analyze" class="btn btn-outline-success btn-sm">Optimize Batch</button>
|
||||
<!-- <button id="button_stopall" class="btn btn-outline-success btn-sm">Stop All</button>
|
||||
<button id="button_refresh" class="btn btn-outline-success btn-sm">Refresh</button> -->
|
||||
</div>
|
||||
|
||||
@ -210,6 +210,7 @@ $(document).ready(function () {
|
||||
$('#button_runagain_arch').attr('disabled','disabled');
|
||||
$('#button_show_arch').attr('disabled','disabled');
|
||||
$('#button_delete_arch').attr('disabled','disabled');
|
||||
$('#button_analyze').attr('disabled','disabled');
|
||||
$('#button_edit_arch').attr('disabled','disabled');
|
||||
$('#button_compare_arch').attr('disabled','disabled');
|
||||
|
||||
@ -218,6 +219,7 @@ $(document).ready(function () {
|
||||
if ($(this).hasClass('selected')) {
|
||||
//$(this).removeClass('selected');
|
||||
$('#button_show_arch').attr('disabled','disabled');
|
||||
$('#button_analyze').attr('disabled','disabled');
|
||||
$('#button_runagain_arch').attr('disabled','disabled');
|
||||
$('#button_delete_arch').attr('disabled','disabled');
|
||||
$('#button_edit_arch').attr('disabled','disabled');
|
||||
@ -225,6 +227,7 @@ $(document).ready(function () {
|
||||
} else {
|
||||
//archiveRecords.$('tr.selected').removeClass('selected');
|
||||
$(this).addClass('selected');
|
||||
$('#button_analyze').attr('disabled',false);
|
||||
$('#button_show_arch').attr('disabled',false);
|
||||
$('#button_runagain_arch').attr('disabled',false);
|
||||
$('#button_delete_arch').attr('disabled',false);
|
||||
@ -413,12 +416,50 @@ $(document).ready(function () {
|
||||
}
|
||||
});
|
||||
|
||||
//generate batch optimization cutoff (predelat na button pro obecne analyzy batche)
|
||||
$('#button_analyze').click(function () {
|
||||
row = archiveRecords.row('.selected').data();
|
||||
if (row == undefined || row.batch_id == undefined) {
|
||||
return
|
||||
}
|
||||
console.log(row)
|
||||
$('#button_analyze').attr('disabled','disabled');
|
||||
$.ajax({
|
||||
url:"/batches/optimizecutoff/"+row.batch_id,
|
||||
beforeSend: function (xhr) {
|
||||
xhr.setRequestHeader('X-API-Key',
|
||||
API_KEY); },
|
||||
method:"POST",
|
||||
xhrFields: {
|
||||
responseType: 'blob'
|
||||
},
|
||||
contentType: "application/json",
|
||||
processData: false,
|
||||
data: JSON.stringify(row.batch_id),
|
||||
success:function(blob){
|
||||
var url = window.URL || window.webkitURL;
|
||||
console.log("vraceny obraz", blob)
|
||||
console.log("url",url.createObjectURL(blob))
|
||||
display_image(url.createObjectURL(blob))
|
||||
$('#button_analyze').attr('disabled',false);
|
||||
},
|
||||
error: function(xhr, status, error) {
|
||||
console.log("proc to skace do erroru?")
|
||||
//window.alert(JSON.stringify(xhr));
|
||||
console.log(JSON.stringify(xhr));
|
||||
$('#button_analyze').attr('disabled',false);
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
|
||||
//generate report button
|
||||
$('#button_report').click(function () {
|
||||
rows = archiveRecords.rows('.selected');
|
||||
if (rows == undefined) {
|
||||
return
|
||||
}
|
||||
$('#button_report').attr('disabled','disabled');
|
||||
runnerIds = []
|
||||
if(rows.data().length > 0 ) {
|
||||
// Loop through the selected rows and display an alert with each row's ID
|
||||
@ -444,11 +485,13 @@ $(document).ready(function () {
|
||||
console.log("vraceny obraz", blob)
|
||||
console.log("url",url.createObjectURL(blob))
|
||||
display_image(url.createObjectURL(blob))
|
||||
$('#button_report').attr('disabled',false);
|
||||
},
|
||||
error: function(xhr, status, error) {
|
||||
console.log("proc to skace do erroru?")
|
||||
//window.alert(JSON.stringify(xhr));
|
||||
console.log(JSON.stringify(xhr));
|
||||
$('#button_report').attr('disabled',false);
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
Reference in New Issue
Block a user