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pycryptobot.py
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#!/usr/bin/env python3
# encoding: utf-8
"""Python Crypto Bot consuming Coinbase Pro or Binance APIs"""
import functools
import os
import sched
import sys
import time
import signal
import json
from datetime import datetime, timedelta
import pandas as pd
from models.AppState import AppState
from models.exchange.Granularity import Granularity
from models.helper.LogHelper import Logger
from models.helper.MarginHelper import calculate_margin
from models.PyCryptoBot import PyCryptoBot
from models.PyCryptoBot import truncate as _truncate
from models.Stats import Stats
from models.Strategy import Strategy
from models.Trading import TechnicalAnalysis
from models.TradingAccount import TradingAccount
from views.TradingGraphs import TradingGraphs
from models.helper.TextBoxHelper import TextBox
from models.exchange.ExchangesEnum import Exchange
from models.exchange.binance import WebSocketClient as BWebSocketClient
from models.exchange.coinbase_pro import WebSocketClient as CWebSocketClient
from models.exchange.kucoin import WebSocketClient as KWebSocketClient
from models.helper.TelegramBotHelper import TelegramBotHelper
# minimal traceback
sys.tracebacklimit = 1
app = PyCryptoBot()
account = TradingAccount(app)
Stats(app, account).show()
technical_analysis = None
state = AppState(app, account)
state.initLastAction()
telegram_bot = TelegramBotHelper(app)
s = sched.scheduler(time.time, time.sleep)
def signal_handler(signum, frame):
if signum == 2:
print("Please be patient while websockets terminate!")
#Logger.debug(frame)
return
def executeJob(
sc=None,
_app: PyCryptoBot = None,
_state: AppState = None,
_technical_analysis=None,
_websocket=None,
trading_data=pd.DataFrame(),
):
"""Trading bot job which runs at a scheduled interval"""
# This is used to control some API calls when using websockets
last_api_call_datetime = datetime.now() - _state.last_api_call_datetime
if last_api_call_datetime.seconds > 60:
_state.last_api_call_datetime = datetime.now()
# This is used by the telegram bot
# If it not enabled in config while will always be False
if not _app.isSimulation():
controlstatus = telegram_bot.checkbotcontrolstatus()
while controlstatus == "pause" or controlstatus == "paused":
if controlstatus == "pause":
print(str(datetime.now()).format() + " - Bot is paused")
_app.notifyTelegram(f"{_app.getMarket()} bot is paused")
telegram_bot.updatebotstatus("paused")
if _app.enableWebsocket():
Logger.info("Stopping _websocket...")
_websocket.close()
time.sleep(30)
controlstatus = telegram_bot.checkbotcontrolstatus()
if controlstatus == "start":
print(str(datetime.now()).format() + " - Bot has restarted")
_app.notifyTelegram(f"{_app.getMarket()} bot has restarted")
telegram_bot.updatebotstatus("active")
_app.read_config(_app.getExchange())
if _app.enableWebsocket():
Logger.info("Starting _websocket...")
_websocket.start()
if controlstatus == "exit":
_app.notifyTelegram(f"{_app.getMarket()} bot is stopping")
sys.exit(0)
# reset _websocket every 23 hours if applicable
if _app.enableWebsocket() and not _app.isSimulation():
if _websocket.getTimeElapsed() > 82800:
Logger.info("Websocket requires a restart every 23 hours!")
Logger.info("Stopping _websocket...")
_websocket.close()
Logger.info("Starting _websocket...")
_websocket.start()
Logger.info("Restarting job in 30 seconds...")
s.enter(
30, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket)
)
# increment _state.iterations
_state.iterations = _state.iterations + 1
if not _app.isSimulation():
# retrieve the _app.getMarket() data
trading_data = _app.getHistoricalData(
_app.getMarket(), _app.getGranularity(), _websocket
)
else:
if len(trading_data) == 0:
return None
# analyse the market data
if _app.isSimulation() and len(trading_data.columns) > 8:
df = trading_data
if _app.appStarted and _app.simstartdate is not None:
# On first run set the iteration to the start date entered
# This sim mode now pulls 300 candles from before the entered start date
_state.iterations = (
df.index.get_loc(str(_app.getDateFromISO8601Str(_app.simstartdate))) + 1
)
_app.appStarted = False
# if smartswitch then get the market data using new granularity
if _app.sim_smartswitch:
df_last = _app.getInterval(df, _state.iterations)
if len(df_last.index.format()) > 0:
if _app.simstartdate is not None:
startDate = _app.getDateFromISO8601Str(_app.simstartdate)
else:
startDate = _app.getDateFromISO8601Str(
str(df.head(1).index.format()[0])
)
if _app.simenddate is not None:
if _app.simenddate == "now":
endDate = _app.getDateFromISO8601Str(str(datetime.now()))
else:
endDate = _app.getDateFromISO8601Str(_app.simenddate)
else:
endDate = _app.getDateFromISO8601Str(
str(df.tail(1).index.format()[0])
)
simDate = _app.getDateFromISO8601Str(str(_state.last_df_index))
trading_data = _app.getSmartSwitchHistoricalDataChained(
_app.getMarket(),
_app.getGranularity(),
str(startDate),
str(endDate),
)
if _app.getGranularity() == Granularity.ONE_HOUR:
simDate = _app.getDateFromISO8601Str(str(simDate))
sim_rounded = pd.Series(simDate).dt.round("60min")
simDate = sim_rounded[0]
elif _app.getGranularity() == Granularity.FIFTEEN_MINUTES:
simDate = _app.getDateFromISO8601Str(str(simDate))
sim_rounded = pd.Series(simDate).dt.round("15min")
simDate = sim_rounded[0]
elif _app.getGranularity() == Granularity.FIVE_MINUTES:
simDate = _app.getDateFromISO8601Str(str(simDate))
sim_rounded = pd.Series(simDate).dt.round("5min")
simDate = sim_rounded[0]
dateFound = False
while dateFound == False:
try:
_state.iterations = trading_data.index.get_loc(str(simDate)) + 1
dateFound = True
except:
simDate += timedelta(seconds=_app.getGranularity().value[0])
if (
_app.getDateFromISO8601Str(str(simDate)).isoformat()
== _app.getDateFromISO8601Str(str(_state.last_df_index)).isoformat()
):
_state.iterations += 1
if _state.iterations == 0:
_state.iterations = 1
trading_dataCopy = trading_data.copy()
_technical_analysis = TechnicalAnalysis(trading_dataCopy)
# if 'morning_star' not in df:
_technical_analysis.addAll()
df = _technical_analysis.getDataFrame()
_app.sim_smartswitch = False
elif _app.getSmartSwitch() == 1 and _technical_analysis is None:
trading_dataCopy = trading_data.copy()
_technical_analysis = TechnicalAnalysis(trading_dataCopy)
if "morning_star" not in df:
_technical_analysis.addAll()
df = _technical_analysis.getDataFrame()
else:
trading_dataCopy = trading_data.copy()
_technical_analysis = TechnicalAnalysis(trading_dataCopy)
_technical_analysis.addAll()
df = _technical_analysis.getDataFrame()
if _app.isSimulation() and _app.appStarted:
# On first run set the iteration to the start date entered
# This sim mode now pulls 300 candles from before the entered start date
_state.iterations = (
df.index.get_loc(str(_app.getDateFromISO8601Str(_app.simstartdate))) + 1
)
_app.appStarted = False
if _app.isSimulation():
df_last = _app.getInterval(df, _state.iterations)
else:
df_last = _app.getInterval(df)
if len(df_last.index.format()) > 0:
current_df_index = str(df_last.index.format()[0])
else:
current_df_index = _state.last_df_index
formatted_current_df_index = (
f"{current_df_index} 00:00:00"
if len(current_df_index) == 10
else current_df_index
)
current_sim_date = formatted_current_df_index
if _state.iterations == 2:
# check if bot has open or closed order
# update data.json "opentrades"
if _state.last_action == "BUY":
telegram_bot.add_open_order()
else:
telegram_bot.remove_open_order()
if (
(last_api_call_datetime.seconds > 60 or _app.isSimulation())
and _app.getSmartSwitch() == 1
and _app.getSellSmartSwitch() == 1
and _app.getGranularity() != Granularity.FIVE_MINUTES
and _state.last_action == "BUY"
):
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(
"*** open order detected smart switching to 300 (5 min) granularity ***"
)
if not _app.telegramTradesOnly():
_app.notifyTelegram(
_app.getMarket()
+ " open order detected smart switching to 300 (5 min) granularity"
)
if _app.isSimulation():
_app.sim_smartswitch = True
_app.setGranularity(Granularity.FIVE_MINUTES)
list(map(s.cancel, s.queue))
s.enter(5, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket))
if (
(last_api_call_datetime.seconds > 60 or _app.isSimulation())
and _app.getSmartSwitch() == 1
and _app.getSellSmartSwitch() == 1
and _app.getGranularity() == Granularity.FIVE_MINUTES
and _state.last_action == "SELL"
):
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(
"*** sell detected smart switching to 3600 (1 hour) granularity ***"
)
if not _app.telegramTradesOnly():
_app.notifyTelegram(
_app.getMarket()
+ " sell detected smart switching to 3600 (1 hour) granularity"
)
if _app.isSimulation():
_app.sim_smartswitch = True
_app.setGranularity(Granularity.ONE_HOUR)
list(map(s.cancel, s.queue))
s.enter(5, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket))
# use actual sim mode date to check smartchswitch
if (
(last_api_call_datetime.seconds > 60 or _app.isSimulation())
and _app.getSmartSwitch() == 1
and _app.getGranularity() == Granularity.ONE_HOUR
and _app.is1hEMA1226Bull(current_sim_date, _websocket) is True
and _app.is6hEMA1226Bull(current_sim_date, _websocket) is True
):
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(
"*** smart switch from granularity 3600 (1 hour) to 900 (15 min) ***"
)
if _app.isSimulation():
_app.sim_smartswitch = True
if not _app.telegramTradesOnly():
_app.notifyTelegram(
_app.getMarket()
+ " smart switch from granularity 3600 (1 hour) to 900 (15 min)"
)
_app.setGranularity(Granularity.FIFTEEN_MINUTES)
list(map(s.cancel, s.queue))
s.enter(5, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket))
# use actual sim mode date to check smartchswitch
if (
(last_api_call_datetime.seconds > 60 or _app.isSimulation())
and _app.getSmartSwitch() == 1
and _app.getGranularity() == Granularity.FIFTEEN_MINUTES
and _app.is1hEMA1226Bull(current_sim_date, _websocket) is False
and _app.is6hEMA1226Bull(current_sim_date, _websocket) is False
):
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(
"*** smart switch from granularity 900 (15 min) to 3600 (1 hour) ***"
)
if _app.isSimulation():
_app.sim_smartswitch = True
if not _app.telegramTradesOnly():
_app.notifyTelegram(
f"{_app.getMarket()} smart switch from granularity 900 (15 min) to 3600 (1 hour)"
)
_app.setGranularity(Granularity.ONE_HOUR)
list(map(s.cancel, s.queue))
s.enter(5, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket))
if (
_app.getExchange() == Exchange.BINANCE
and _app.getGranularity() == Granularity.ONE_DAY
):
if len(df) < 250:
# data frame should have 250 rows, if not retry
Logger.error(f"error: data frame length is < 250 ({str(len(df))})")
list(map(s.cancel, s.queue))
s.enter(
300, 1, executeJob, (sc, _app, _state, _technical_analysis, _websocket)
)
else:
if len(df) < 300:
if not _app.isSimulation():
# data frame should have 300 rows, if not retry
Logger.error(f"error: data frame length is < 300 ({str(len(df))})")
list(map(s.cancel, s.queue))
s.enter(
300,
1,
executeJob,
(sc, _app, _state, _technical_analysis, _websocket),
)
if len(df_last) > 0:
now = datetime.today().strftime("%Y-%m-%d %H:%M:%S")
# last_action polling if live
if _app.isLive():
last_action_current = _state.last_action
# If using websockets make this call every minute instead of each iteration
if _app.enableWebsocket() and not _app.isSimulation():
if last_api_call_datetime.seconds > 60:
_state.pollLastAction()
else:
_state.pollLastAction()
if last_action_current != _state.last_action:
Logger.info(
f"last_action change detected from {last_action_current} to {_state.last_action}"
)
if not _app.telegramTradesOnly():
_app.notifyTelegram(
f"{_app.getMarket} last_action change detected from {last_action_current} to {_state.last_action}"
)
if not _app.isSimulation():
ticker = _app.getTicker(_app.getMarket(), _websocket)
now = ticker[0]
price = ticker[1]
if price < df_last["low"].values[0] or price == 0:
price = float(df_last["close"].values[0])
else:
price = float(df_last["close"].values[0])
if price < 0.000001:
raise Exception(
f"{_app.getMarket()} is unsuitable for trading, quote price is less than 0.000001!"
)
# technical indicators
ema12gtema26 = bool(df_last["ema12gtema26"].values[0])
ema12gtema26co = bool(df_last["ema12gtema26co"].values[0])
goldencross = bool(df_last["goldencross"].values[0])
macdgtsignal = bool(df_last["macdgtsignal"].values[0])
macdgtsignalco = bool(df_last["macdgtsignalco"].values[0])
ema12ltema26 = bool(df_last["ema12ltema26"].values[0])
ema12ltema26co = bool(df_last["ema12ltema26co"].values[0])
macdltsignal = bool(df_last["macdltsignal"].values[0])
macdltsignalco = bool(df_last["macdltsignalco"].values[0])
obv = float(df_last["obv"].values[0])
obv_pc = float(df_last["obv_pc"].values[0])
elder_ray_buy = bool(df_last["eri_buy"].values[0])
elder_ray_sell = bool(df_last["eri_sell"].values[0])
# if simulation, set goldencross based on actual sim date
if _app.isSimulation():
goldencross = _app.is1hSMA50200Bull(current_sim_date, _websocket)
# candlestick detection
hammer = bool(df_last["hammer"].values[0])
inverted_hammer = bool(df_last["inverted_hammer"].values[0])
hanging_man = bool(df_last["hanging_man"].values[0])
shooting_star = bool(df_last["shooting_star"].values[0])
three_white_soldiers = bool(df_last["three_white_soldiers"].values[0])
three_black_crows = bool(df_last["three_black_crows"].values[0])
morning_star = bool(df_last["morning_star"].values[0])
evening_star = bool(df_last["evening_star"].values[0])
three_line_strike = bool(df_last["three_line_strike"].values[0])
abandoned_baby = bool(df_last["abandoned_baby"].values[0])
morning_doji_star = bool(df_last["morning_doji_star"].values[0])
evening_doji_star = bool(df_last["evening_doji_star"].values[0])
two_black_gapping = bool(df_last["two_black_gapping"].values[0])
# Log data for Telegram Bot
telegram_bot.addindicators("EMA", ema12gtema26co or ema12ltema26)
if not _app.disableBuyElderRay():
telegram_bot.addindicators("ERI", elder_ray_buy)
if _app.disableBullOnly():
telegram_bot.addindicators("BULL", goldencross)
if not _app.disableBuyMACD():
telegram_bot.addindicators("MACD", macdgtsignal or macdgtsignalco)
if not _app.disableBuyOBV():
telegram_bot.addindicators("OBV", float(obv_pc) > 0)
if _app.isSimulation():
# Reset the Strategy so that the last record is the current sim date
# To allow for calculations to be done on the sim date being processed
sdf = df[df["date"] <= current_sim_date].tail(300)
strategy = Strategy(
_app, _state, sdf, sdf.index.get_loc(str(current_sim_date)) + 1
)
else:
strategy = Strategy(_app, _state, df, _state.iterations)
_state.action = strategy.getAction(_app, price, current_sim_date)
immediate_action = False
margin, profit, sell_fee = 0, 0, 0
# Reset the TA so that the last record is the current sim date
# To allow for calculations to be done on the sim date being processed
if _app.isSimulation():
trading_dataCopy = (
trading_data[trading_data["date"] <= current_sim_date].tail(300).copy()
)
_technical_analysis = TechnicalAnalysis(trading_dataCopy)
if (
_state.last_buy_size > 0
and _state.last_buy_price > 0
and price > 0
and _state.last_action == "BUY"
):
# update last buy high
if price > _state.last_buy_high:
_state.last_buy_high = price
if _state.last_buy_high > 0:
change_pcnt_high = ((price / _state.last_buy_high) - 1) * 100
else:
change_pcnt_high = 0
# buy and sell calculations
_state.last_buy_fee = round(_state.last_buy_size * _app.getTakerFee(), 8)
_state.last_buy_filled = round(
((_state.last_buy_size - _state.last_buy_fee) / _state.last_buy_price),
8,
)
# if not a simulation, sync with exchange orders
if not _app.isSimulation():
if _app.enableWebsocket():
if last_api_call_datetime.seconds > 60:
_state.exchange_last_buy = _app.getLastBuy()
else:
_state.exchange_last_buy = _app.getLastBuy()
exchange_last_buy = _state.exchange_last_buy
if exchange_last_buy is not None:
if _state.last_buy_size != exchange_last_buy["size"]:
_state.last_buy_size = exchange_last_buy["size"]
if _state.last_buy_filled != exchange_last_buy["filled"]:
_state.last_buy_filled = exchange_last_buy["filled"]
if _state.last_buy_price != exchange_last_buy["price"]:
_state.last_buy_price = exchange_last_buy["price"]
if (
_app.getExchange() == Exchange.COINBASEPRO
or _app.getExchange() == Exchange.KUCOIN
):
if _state.last_buy_fee != exchange_last_buy["fee"]:
_state.last_buy_fee = exchange_last_buy["fee"]
margin, profit, sell_fee = calculate_margin(
buy_size=_state.last_buy_size,
buy_filled=_state.last_buy_filled,
buy_price=_state.last_buy_price,
buy_fee=_state.last_buy_fee,
sell_percent=_app.getSellPercent(),
sell_price=price,
sell_taker_fee=_app.getTakerFee(),
)
# handle immediate sell actions
if strategy.isSellTrigger(
_app,
price,
_technical_analysis.getTradeExit(price),
margin,
change_pcnt_high,
obv_pc,
macdltsignal,
):
_state.action = "SELL"
_state.last_action = "BUY"
immediate_action = True
# handle overriding wait actions (e.g. do not sell if sell at loss disabled!, do not buy in bull if bull only)
if strategy.isWaitTrigger(_app, margin, goldencross):
_state.action = "WAIT"
immediate_action = False
if _app.enableImmediateBuy():
if _state.action == "BUY":
immediate_action = True
if _state.action == "WAIT":
manual_buy_sell = telegram_bot.checkmanualbuysell()
if not manual_buy_sell == "WAIT":
_state.action = manual_buy_sell
_state.last_action = "BUY" if _state.action == "SELL" else "SELL"
immediate_action = True
# If buy signal, save the price and check for decrease/increase before buying.
trailing_buy_logtext = ""
if _state.action == "BUY" and immediate_action != True:
_state.action, _state.trailing_buy, trailing_buy_logtext = strategy.checkTrailingBuy(_app, _state, price)
bullbeartext = ""
if _app.disableBullOnly() is True or (
df_last["sma50"].values[0] == df_last["sma200"].values[0]
):
bullbeartext = ""
elif goldencross is True:
bullbeartext = " (BULL)"
elif goldencross is False:
bullbeartext = " (BEAR)"
# polling is every 5 minutes (even for hourly intervals), but only process once per interval
# Logger.debug("DateCheck: " + str(immediate_action) + ' ' + str(_state.last_df_index) + ' ' + str(current_df_index))
if immediate_action is True or _state.last_df_index != current_df_index:
text_box = TextBox(80, 22)
precision = 4
if price < 0.01:
precision = 8
# Since precision does not change after this point, it is safe to prepare a tailored `truncate()` that would
# work with this precision. It should save a couple of `precision` uses, one for each `truncate()` call.
truncate = functools.partial(_truncate, n=precision)
price_text = "Close: " + str(price)
ema_text = ""
if _app.disableBuyEMA() is False:
ema_text = _app.compare(
df_last["ema12"].values[0],
df_last["ema26"].values[0],
"EMA12/26",
precision,
)
macd_text = ""
if _app.disableBuyMACD() is False:
macd_text = _app.compare(
df_last["macd"].values[0],
df_last["signal"].values[0],
"MACD",
precision,
)
obv_text = ""
if _app.disableBuyOBV() is False:
obv_text = (
"OBV: "
+ truncate(df_last["obv"].values[0])
+ " ("
+ str(truncate(df_last["obv_pc"].values[0]))
+ "%)"
)
_state.eri_text = ""
if _app.disableBuyElderRay() is False:
if elder_ray_buy is True:
_state.eri_text = "ERI: buy | "
elif elder_ray_sell is True:
_state.eri_text = "ERI: sell | "
else:
_state.eri_text = "ERI: | "
log_text = ""
if hammer is True:
log_text = '* Candlestick Detected: Hammer ("Weak - Reversal - Bullish Signal - Up")'
if shooting_star is True:
log_text = '* Candlestick Detected: Shooting Star ("Weak - Reversal - Bearish Pattern - Down")'
if hanging_man is True:
log_text = '* Candlestick Detected: Hanging Man ("Weak - Continuation - Bearish Pattern - Down")'
if inverted_hammer is True:
log_text = '* Candlestick Detected: Inverted Hammer ("Weak - Continuation - Bullish Pattern - Up")'
if three_white_soldiers is True:
log_text = '*** Candlestick Detected: Three White Soldiers ("Strong - Reversal - Bullish Pattern - Up")'
if three_black_crows is True:
log_text = '* Candlestick Detected: Three Black Crows ("Strong - Reversal - Bearish Pattern - Down")'
if morning_star is True:
log_text = '*** Candlestick Detected: Morning Star ("Strong - Reversal - Bullish Pattern - Up")'
if evening_star is True:
log_text = '*** Candlestick Detected: Evening Star ("Strong - Reversal - Bearish Pattern - Down")'
if three_line_strike is True:
log_text = '** Candlestick Detected: Three Line Strike ("Reliable - Reversal - Bullish Pattern - Up")'
if abandoned_baby is True:
log_text = '** Candlestick Detected: Abandoned Baby ("Reliable - Reversal - Bullish Pattern - Up")'
if morning_doji_star is True:
log_text = '** Candlestick Detected: Morning Doji Star ("Reliable - Reversal - Bullish Pattern - Up")'
if evening_doji_star is True:
log_text = '** Candlestick Detected: Evening Doji Star ("Reliable - Reversal - Bearish Pattern - Down")'
if two_black_gapping is True:
log_text = '*** Candlestick Detected: Two Black Gapping ("Reliable - Reversal - Bearish Pattern - Down")'
if (
log_text != ""
and not _app.isSimulation()
or (_app.isSimulation() and not _app.simResultOnly())
):
Logger.info(log_text)
ema_co_prefix = ""
ema_co_suffix = ""
if _app.disableBuyEMA() is False:
if ema12gtema26co is True:
ema_co_prefix = "*^ "
ema_co_suffix = " ^* | "
elif ema12ltema26co is True:
ema_co_prefix = "*v "
ema_co_suffix = " v* | "
elif ema12gtema26 is True:
ema_co_prefix = "^ "
ema_co_suffix = " ^ | "
elif ema12ltema26 is True:
ema_co_prefix = "v "
ema_co_suffix = " v | "
macd_co_prefix = ""
macd_co_suffix = ""
if _app.disableBuyMACD() is False:
if macdgtsignalco is True:
macd_co_prefix = "*^ "
macd_co_suffix = " ^* | "
elif macdltsignalco is True:
macd_co_prefix = "*v "
macd_co_suffix = " v* | "
elif macdgtsignal is True:
macd_co_prefix = "^ "
macd_co_suffix = " ^ | "
elif macdltsignal is True:
macd_co_prefix = "v "
macd_co_suffix = " v | "
obv_prefix = ""
obv_suffix = ""
if _app.disableBuyOBV() is False:
if float(obv_pc) > 0:
obv_prefix = "^ "
obv_suffix = " ^ | "
elif float(obv_pc) < 0:
obv_prefix = "v "
obv_suffix = " v | "
else:
obv_suffix = " | "
if not _app.isVerbose():
if _state.last_action != "":
# Not sure if this if is needed just preserving any existing functionality that may have been missed
# Updated to show over margin and profit
if not _app.isSimulation():
output_text = (
formatted_current_df_index
+ " | "
+ _app.getMarket()
+ bullbeartext
+ " | "
+ _app.printGranularity()
+ " | "
+ price_text
+ trailing_buy_logtext
+ " | "
+ ema_co_prefix
+ ema_text
+ ema_co_suffix
+ macd_co_prefix
+ macd_text
+ macd_co_suffix
+ obv_prefix
+ obv_text
+ obv_suffix
+ _state.eri_text
+ _state.action
+ " | Last Action: "
+ _state.last_action
+ " | DF HIGH: "
+ str(df["close"].max())
+ " | "
+ "DF LOW: "
+ str(df["close"].min())
+ " | SWING: "
+ str(
round(
(
(df["close"].max() - df["close"].min())
/ df["close"].min()
)
* 100,
2,
)
)
+ "% |"
+ " CURR Price is "
+ str(
round(
((price - df["close"].max()) / df["close"].max())
* 100,
2,
)
)
+ "% "
+ "away from DF HIGH | Range: "
+ str(df.iloc[0, 0])
+ " <--> "
+ str(df.iloc[len(df) - 1, 0])
)
else:
df_high = df[df["date"] <= current_sim_date]["close"].max()
df_low = df[df["date"] <= current_sim_date]["close"].min()
# print(df_high)
output_text = (
formatted_current_df_index
+ " | "
+ _app.getMarket()
+ bullbeartext
+ " | "
+ _app.printGranularity()
+ " | "
+ price_text
+ trailing_buy_logtext
+ " | "
+ ema_co_prefix
+ ema_text
+ ema_co_suffix
+ macd_co_prefix
+ macd_text
+ macd_co_suffix
+ obv_prefix
+ obv_text
+ obv_suffix
+ _state.eri_text
+ _state.action
+ " | Last Action: "
+ _state.last_action
+ " | DF HIGH: "
+ str(df_high)
+ " | "
+ "DF LOW: "
+ str(df_low)
+ " | SWING: "
+ str(round(((df_high - df_low) / df_low) * 100, 2))
+ "% |"
+ " CURR Price is "
+ str(round(((price - df_high) / df_high) * 100, 2))
+ "% "
+ "away from DF HIGH | Range: "
+ str(df.iloc[_state.iterations - 300, 0])
+ " <--> "
+ str(df.iloc[_state.iterations - 1, 0])
)
else:
if not _app.isSimulation:
output_text = (
formatted_current_df_index
+ " | "
+ _app.getMarket()
+ bullbeartext
+ " | "
+ _app.printGranularity()
+ " | "
+ price_text
+ trailing_buy_logtext
+ " | "
+ ema_co_prefix
+ ema_text
+ ema_co_suffix
+ macd_co_prefix
+ macd_text
+ macd_co_suffix
+ obv_prefix
+ obv_text
+ obv_suffix
+ _state.eri_text
+ _state.action
+ " | DF HIGH: "
+ str(df["close"].max())
+ " | "
+ "DF LOW: "
+ str(df["close"].min())
+ " | SWING: "
+ str(
round(
(
(df["close"].max() - df["close"].min())
/ df["close"].min()
)
* 100,
2,
)
)
+ "%"
+ " CURR Price is "
+ str(
round(
((price - df["close"].max()) / df["close"].max())
* 100,
2,
)
)
+ "% "
+ "away from DF HIGH | Range: "
+ str(df.iloc[0, 0])
+ " <--> "
+ str(df.iloc[len(df) - 1, 0])
)
else:
df_high = df[df["date"] <= current_sim_date]["close"].max()
df_low = df[df["date"] <= current_sim_date]["close"].min()
output_text = (
formatted_current_df_index
+ " | "
+ _app.getMarket()
+ bullbeartext
+ " | "
+ _app.printGranularity()
+ " | "
+ price_text
+ trailing_buy_logtext
+ " | "
+ ema_co_prefix
+ ema_text
+ ema_co_suffix
+ macd_co_prefix
+ macd_text
+ macd_co_suffix
+ obv_prefix
+ obv_text
+ obv_suffix
+ _state.eri_text
+ _state.action
+ " | DF HIGH: "
+ str(df_high)
+ " | "
+ "DF LOW: "
+ str(df_low)
+ " | SWING: "
+ str(round(((df_high - df_low) / df_low) * 100, 2))
+ "%"
+ " CURR Price is "
+ str(round(((price - df_high) / df_high) * 100, 2))
+ "% "
+ "away from DF HIGH | Range: "
+ str(df.iloc[_state.iterations - 300, 0])
+ " <--> "
+ str(df.iloc[_state.iterations - 1, 0])
)
if _state.last_action == "BUY":
if _state.last_buy_size > 0:
margin_text = truncate(margin) + "%"
else:
margin_text = "0%"
output_text += (
" | (margin: "
+ margin_text
+ " delta: "
+ str(round(price - _state.last_buy_price, precision))
+ ")"
)
if _app.isSimulation():
# save margin for Summary if open trade
_state.open_trade_margin = margin_text
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(output_text)
if _app.enableML():
# Seasonal Autoregressive Integrated Moving Average (ARIMA) model (ML prediction for 3 intervals from now)
if not _app.isSimulation():
try:
prediction = (
_technical_analysis.seasonalARIMAModelPrediction(
int(_app.getGranularity().to_integer / 60) * 3
)
) # 3 intervals from now
Logger.info(
f"Seasonal ARIMA model predicts the closing price will be {str(round(prediction[1], 2))} at {prediction[0]} (delta: {round(prediction[1] - price, 2)})"
)
# pylint: disable=bare-except
except:
pass
if _state.last_action == "BUY":
# display support, resistance and fibonacci levels
if not _app.isSimulation() or (
_app.isSimulation() and not _app.simResultOnly()
):
Logger.info(
_technical_analysis.printSupportResistanceFibonacciLevels(
price
)
)
else:
# set to true for verbose debugging
debug = False
if debug:
Logger.debug(f"-- Iteration: {str(_state.iterations)} --{bullbeartext}")
if _state.last_action == "BUY":
if _state.last_buy_size > 0:
margin_text = truncate(margin) + "%"
else:
margin_text = "0%"
if _app.isSimulation():
# save margin for Summary if open trade
_state.open_trade_margin = margin_text
if debug:
Logger.debug(f"-- Margin: {margin_text} --")