from pathlib import Path from typing import Union import json from datetime import datetime, date, timedelta import re import pandas as pd PACKAGE_ROOT = Path(__file__).parent.parent #PACKAGE_PARENT = '..' #SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__)))) #sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT))) #PACKAGE_ROOT = os.path.dirname(os.path.abspath(__file__)) class App: """Globally visible variables.""" # System loop = None # asyncio main loop sched = None # Scheduler analyzer = None # Store and analyze data # Connector client client = None # WebSocket for push notifications bm = None conn_key = None # Socket # # State of the server (updated after each interval) # # State 0 or None or empty means ok. String and other non empty objects mean error error_status = 0 # Networks, connections, exceptions etc. what does not allow us to work at all server_status = 0 # If server allow us to trade (maintenance, down etc.) account_status = 0 # If account allows us to trade (funds, suspended etc.) trade_state_status = 0 # Something wrong with our trading logic (wrong use, inconsistent state etc. what we cannot recover) df = None # Data from the latest analysis # Trade simulator transaction = None # Trade binance status = None # BOUGHT, SOLD, BUYING, SELLING order = None # Latest or current order order_time = None # Order submission time # Available assets for trade # Can be set by the sync/recover function or updated by the trading algorithm base_quantity = "0.04108219" # BTC owned (on account, already bought, available for trade) quote_quantity = "1000.0" # USDT owned (on account, available for trade) # # Trader. Status data retrieved from the server. Below are examples only. # system_status = {"status": 0, "msg": "normal"} # 0: normal，1：system maintenance symbol_info = {} account_info = {} # # Constant configuration parameters # config = { # Binance "api_key": "", "api_secret": "", # Telegram "telegram_bot_token": "", # Source address of messages "telegram_chat_id": "", # Destination address of messages # # Conventions for the file and column names # "merge_file_name": "data.csv", "feature_file_name": "features.csv", "matrix_file_name": "matrix.csv", "predict_file_name": "predictions.csv", # predict, predict-rolling "signal_file_name": "signals.csv", "signal_models_file_name": "signal_models", "model_folder": "MODELS", "time_column": "timestamp", # File locations "data_folder": "C:/DATA_ITB", # Location for all source and generated data/models # ============================================== # === DOWNLOADER, MERGER and (online) READER === # Symbol determines sub-folder and used in other identifiers "symbol": "BTCUSDT", # BTCUSDT ETHUSDT ^gspc # This parameter determines time raster (granularity) for the data # It is pandas frequency "freq": "1min", # This list is used for downloading and then merging data # "folder" is symbol name for downloading. prefix will be added column names during merge "data_sources": [], # ========================== # === FEATURE GENERATION === # What columns to pass to which feature generator and how to prefix its derived features # Each executes one feature generation function applied to columns with the specified prefix "feature_sets": [], # ======================== # === LABEL GENERATION === "label_sets": [], # =========================== # === MODEL TRAIN/PREDICT === # predict off-line and on-line "label_horizon": 0, # This number of tail rows will be excluded from model training "train_length": 0, # train set maximum size. algorithms may decrease this length # List all features to be used for training/prediction by selecting them from the result of feature generation # The list of features can be found in the output of the feature generation (but not all must be used) # Currently the same feature set for all algorithms "train_features": [], # Labels to be used for training/prediction by all algorithms # List of available labels can be found in the output of the label generation (but not all must be used) "labels": [], # Algorithms and their configurations to be used for training/prediction "algorithms": [], # =========================== # ONLINE (PREDICTION) PARAMETERS # Minimum history length required to compute derived features "features_horizon": 10, # =============== # === SIGNALS === "signal_sets": [], # ===================== # === NOTIFICATIONS === "score_notification_model": {}, "diagram_notification_model": {}, # =============== # === TRADING === "trade_model": { "no_trades_only_data_processing": False, # in market or out of market processing is excluded (all below parameters ignored) "test_order_before_submit": False, # Send test submit to the server as part of validation "simulate_order_execution": False, # Instead of real orders, simulate their execution (immediate buy/sell market orders and use high price of klines for limit orders) "percentage_used_for_trade": 99, # in % to the available USDT quantity, that is, we will derive how much BTC to buy using this percentage "limit_price_adjustment": 0.005, # Limit price of orders will be better than the latest close price (0 means no change, positive - better for us, negative - worse for us) }, "simulate_model": {}, # ===================== # === BINANCE TRADER === "base_asset": "", # BTC ETH "quote_asset": "", # ================== # === COLLECTORS === "collector": { "folder": "DATA", "flush_period": 300, # seconds "depth": { "folder": "DEPTH", "symbols": ["BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH"], "limit": 100, # Legal values (depth): '5, 10, 20, 50, 100, 500, 1000, 5000' <100 weight=1 "freq": "1min", # Pandas frequency }, "stream": { "folder": "STREAM", # Stream formats: # For kline channel: @kline_, Event type: "e": "kline", Symbol: "s": "BNBBTC" # For depth channel: @depth[@100ms], Event type: NO, Symbol: NO # btcusdt@ticker "channels": ["kline_1m", "depth20"], # kline_1m, depth20, depth5 "symbols": ["BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH"], # "BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH" } }, } def data_provider_problems_exist(): if App.error_status != 0: return True if App.server_status != 0: return True return False def problems_exist(): if App.error_status != 0: return True if App.server_status != 0: return True if App.account_status != 0: return True if App.trade_state_status != 0: return True return False def load_config(config_file): if config_file: config_file_path = PACKAGE_ROOT / config_file with open(config_file_path, encoding='utf-8') as json_file: #conf_str = json.load(json_file) conf_str = json_file.read() # Remove everything starting with // and till the line end conf_str = re.sub(r"//.*$", "", conf_str, flags=re.M) conf_json = json.loads(conf_str) App.config.update(conf_json) if __name__ == "__main__": pass