from pathlib import Path from typing import Union import json 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 signal = None, # Latest signal "BUY", "SELL" # # 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) # Trade status transaction = None 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 = { "actions": ["notify"], # Values: notify, trade # Binance "api_key": "", "api_secret": "", # Telegram "telegram_bot_token": "", # Source address of messages "telegram_chat_id": "", # Destination address of messages # File locations "data_folder": "", # It is needed for model training # Symbol determines sub-folder and used in other identifiers "symbol": "", # BTCUSDT ETHUSDT # It will be appended to generated file names and denotes config (like config name). # TODO: In future, we could/should change it semantics, and store the generated files in the folder with this name, rather than append a modifier to file names "config_file_modifier": "", # File name conventions "merge_file_modifier": "data", "feature_file_modifier": "features", "matrix_file_modifier": "matrix", "predict_file_modifier": "predictions", # predict, predict-rolling "signal_file_modifier": "performance", # ===================================== # === MERGER and (online) COLLECTOR === # Specify which data sources to merge into one file with all source columns # Format: (folder, file, prefix) - (symbol, data source, column prefix) # These descriptors are also used to retrieve data by collector in online mode - folder name is symbol name #"data_sources": [ # {"folder": "BTCUSDT", "file": "klines", "column_prefix": ""}, #], #"data_sources": [ # {"folder": "BTCUSDT", "file": "klines", "column_prefix": "btc"}, # {"folder": "ETHUSDT", "file": "klines", "column_prefix": "eth"}, #], # ========================== # === FEATURE GENERATION === # What columns to pass to which feature generator and how to prefix its derived features "feature_sets": [ {"column_prefix": "btc", "generator": "klines", "feature_prefix": "btc"}, {"column_prefix": "eth", "generator": "klines", "feature_prefix": "eth"}, ], # Parameters of klines feature generator # If these are changed, then feature names (below) will also have to be changed #"base_window_kline": 40320, # 4 weeks #"windows_kline": [1, 20, 60, 360, 1440, 10080], #"area_windows_kline": [20, 60, 360, 1440, 10080], #"base_window_kline": 20160, # 2 weeks #"windows_kline": [1, 20, 60, 180, 720, 2880], #"area_windows_kline": [60, 120, 180, 720, 2880], #"windows_kline": [1, 30, 120, 360, 1440, 4320], #"area_windows_kline": [30, 120, 360, 1440, 4320], #"windows_kline": [1, 60, 180, 720, 2880, 5760], #"area_windows_kline": [60, 180, 720, 2880, 5760], "base_window_kline": 10080, # 1 week "windows_kline": [1, 10, 30, 180, 720, 1440], "area_windows_kline": [10, 30, 180, 720, 1440], # ======================== # === LABEL GENERATION === "high_low_horizon": 1440, # Parameter of labels: computing max and min for this horizon ahead "top_bot_column_name": "close", # =========================== # === MODEL TRAIN/PREDICT === # off-line and on-line # Minimum history required to compute derived features # It is used in online mode where we need to maintain data window of this size or larger # Take maximum aggregation windows from feature generation code (and add something to be sure that we have all what is needed) # Basically, should be equal to base_window_kline "features_horizon": 10180, # One model is for each (label, train_features, algorithm) # Feature column names returned by the klines feature generator # They are used by train/predict "features_kline": [ 'btc_close_1', 'btc_close_10', 'btc_close_30', 'btc_close_180', 'btc_close_720', 'btc_close_1440', 'btc_close_std_10', 'btc_close_std_30', 'btc_close_std_180', 'btc_close_std_720', 'btc_close_std_1440', 'btc_volume_1', 'btc_volume_10', 'btc_volume_30', 'btc_volume_180', 'btc_volume_720', 'btc_volume_1440', 'btc_span_1', 'btc_span_10', 'btc_span_30', 'btc_span_180', 'btc_span_720', 'btc_span_1440', 'btc_trades_1', 'btc_trades_10', 'btc_trades_30', 'btc_trades_180', 'btc_trades_720', 'btc_trades_1440', 'btc_tb_base_1', 'btc_tb_base_10', 'btc_tb_base_30', 'btc_tb_base_180', 'btc_tb_base_720', 'btc_tb_base_1440', #'btc_close_area_10', 'btc_close_area_30', 'btc_close_area_180', 'btc_close_area_720', 'btc_close_area_1440', 'btc_close_trend_10', 'btc_close_trend_30', 'btc_close_trend_180', 'btc_close_trend_720', 'btc_close_trend_1440', 'btc_volume_trend_10', 'btc_volume_trend_30', 'btc_volume_trend_180', 'btc_volume_trend_720', 'btc_volume_trend_1440', 'eth_close_1', 'eth_close_10', 'eth_close_30', 'eth_close_180', 'eth_close_720', 'eth_close_1440', 'eth_close_std_10', 'eth_close_std_30', 'eth_close_std_180', 'eth_close_std_720', 'eth_close_std_1440', 'eth_volume_1', 'eth_volume_10', 'eth_volume_30', 'eth_volume_180', 'eth_volume_720', 'eth_volume_1440', 'eth_span_1', 'eth_span_10', 'eth_span_30', 'eth_span_180', 'eth_span_720', 'eth_span_1440', 'eth_trades_1', 'eth_trades_10', 'eth_trades_30', 'eth_trades_180', 'eth_trades_720', 'eth_trades_1440', 'eth_tb_base_1', 'eth_tb_base_10', 'eth_tb_base_30', 'eth_tb_base_180', 'eth_tb_base_720', 'eth_tb_base_1440', #'eth_close_area_10', 'eth_close_area_30', 'eth_close_area_180', 'eth_close_area_720', 'eth_close_area_1440', 'eth_close_trend_10', 'eth_close_trend_30', 'eth_close_trend_180', 'eth_close_trend_720', 'eth_close_trend_1440', 'eth_volume_trend_10', 'eth_volume_trend_30', 'eth_volume_trend_180', 'eth_volume_trend_720', 'eth_volume_trend_1440' ], # Used to select columns for training by adding together different feature sets (the lists are defined elswhere in variables like "features_kline") "train_features": ["kline"], # algorithm descriptors from model store "algorithms": ["nn"], # gb, nn, lc - definitions of their parameters are in model store # This will be excluded from model training "label_horizon": 0, # These labels are specified manually and are produced by label generator # Models will be trained for these models "labels": [ "bot2_025", "bot2_05", "bot2_075", "bot2_1", "bot2_125", "bot2_15", "top2_025", "top2_05", "top2_075", "top2_1", "top2_125", "top2_15", ], "_labels": [ "bot3_025", "bot3_05", "bot3_075", "bot3_1", "bot3_125", "bot3_15", "bot3_175", "top3_025", "top3_05", "top3_075", "top3_1", "top3_125", "top3_15", "top3_175", "bot4_025", "bot4_05", "bot4_075", "bot4_1", "bot4_125", "bot4_15", "bot4_175", "bot4_2", "top4_025", "top4_05", "top4_075", "top4_1", "top4_125", "top4_15", "top4_175", "top4_2", "bot5_025", "bot5_05", "bot5_075", "bot5_1", "bot5_125", "bot5_15", "bot5_175", "bot5_2", "bot5_25", "top5_025", "top5_05", "top5_075", "top5_1", "top5_125", "top5_15", "top5_175", "top5_2", "top5_25", 'high_max_180', # Maximum high (relative) "high_10", "high_15", "high_20", "high_25", "high_30", "low_10", "low_15", "low_20", "low_25", "low_30", 'low_min_180', # Minimum low (relative) 'low_01', 'low_02', 'low_03', 'low_04', # Always above 'low_10', 'low_15', 'low_20', 'low_25', # At least one time below 'high_to_low_180', 'close_area_future_60', 'close_area_future_120', 'close_area_future_180', 'close_area_future_300', ], # ========================= # === SIGNAL GENERATION === # These are predicted columns as well as model (pair) names "buy_labels": [], "sell_labels": [], "_buy_labels": ["bot4_1_k_lc", "bot4_15_k_lc", "bot4_2_k_lc", "bot4_25_k_lc", "bot4_3_k_lc"], "_sell_labels": ["top4_1_k_lc", "top4_15_k_lc", "top4_2_k_lc", "top4_25_k_lc", "top4_3_k_lc"], # It defines how signal scores, trade signals, and notification signals will be generated # from point-wise prediction scores for two groups of labels "signal_model": { # First, aggregation in group over various algorithms and label parameters "buy_point_threshold": None, # Second, produce boolean column (optional) "buy_window": 3, # Third, aggregate in time # Now we have the final score "buy_signal_threshold": 0.65, # To decide whether to buy/sell after all aggregations/combinations "buy_notify_threshold": 0.05, # To decide whether to notify (can be an option of individual users/consumers) "combine": "", # "no_combine", "relative", "difference" Find relative/difference "sell_point_threshold": None, "sell_window": 3, "sell_signal_threshold": 0.65, "sell_notify_threshold": 0.05, "trade_icon_step": 0.1, # For each step, one icon added "notify_frequency_minutes": 10, # 1m, 5m, 10m, 15m etc. Minutes will be divided by this number }, # ===================== # === TRADER SERVER === "base_asset": "", # BTC ETH "quote_asset": "", "trader": { # For debugging: determine what parts of code will be executed "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": -0.0001, # Limit price of orders will be better than the latest close price (0 means no change, positive - better for us, negative - worse for us) # Signal model (trade strategy) - currently NOT USED "sell_timeout": 70, # Seconds "percentage_sell_price": 1.018, # our planned profit per trade via limit sell order (part of the model) }, # ================== # === 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": "1m", # Binance standard frequency: 5s, 1m etc. }, "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) as json_file: config_json = json.load(json_file) App.config.update(config_json) if __name__ == "__main__": pass