forked from mafia23/intelligent-trading-bot
312 lines
14 KiB
Python
312 lines
14 KiB
Python
from pathlib import Path
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from typing import Union
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import json
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import pandas as pd
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PACKAGE_ROOT = Path(__file__).parent.parent
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#PACKAGE_PARENT = '..'
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#SCRIPT_DIR = os.path.dirname(os.path.realpath(os.path.join(os.getcwd(), os.path.expanduser(__file__))))
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#sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT)))
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#PACKAGE_ROOT = os.path.dirname(os.path.abspath(__file__))
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class App:
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"""Globally visible variables."""
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# System
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loop = None # asyncio main loop
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sched = None # Scheduler
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analyzer = None # Store and analyze data
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# Connector client
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client = None
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# WebSocket for push notifications
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bm = None
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conn_key = None # Socket
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signal = None, # Latest signal "BUY", "SELL"
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#
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# State of the server (updated after each interval)
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#
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# State 0 or None or empty means ok. String and other non empty objects mean error
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error_status = 0 # Networks, connections, exceptions etc. what does not allow us to work at all
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server_status = 0 # If server allow us to trade (maintenance, down etc.)
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account_status = 0 # If account allows us to trade (funds, suspended etc.)
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trade_state_status = 0 # Something wrong with our trading logic (wrong use, inconsistent state etc. what we cannot recover)
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# Trade status
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transaction = None
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status = None # BOUGHT, SOLD, BUYING, SELLING
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order = None # Latest or current order
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order_time = None # Order submission time
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# Available assets for trade
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# Can be set by the sync/recover function or updated by the trading algorithm
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base_quantity = "0.04108219" # BTC owned (on account, already bought, available for trade)
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quote_quantity = "1000.0" # USDT owned (on account, available for trade)
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#
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# Trader. Status data retrieved from the server. Below are examples only.
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#
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system_status = {"status": 0, "msg": "normal"} # 0: normal，1：system maintenance
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symbol_info = {}
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account_info = {}
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#
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# Constant configuration parameters
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#
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config = {
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"actions": ["notify"], # Values: notify, trade
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# Binance
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"api_key": "",
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"api_secret": "",
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# Telegram
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"telegram_bot_token": "", # Source address of messages
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"telegram_chat_id": "", # Destination address of messages
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# File locations
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"data_folder": "", # It is needed for model training
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# Symbol determines sub-folder and used in other identifiers
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"symbol": "", # BTCUSDT ETHUSDT
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# It will be appended to generated file names and denotes config (like config name).
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# 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
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"config_file_modifier": "",
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# File name conventions
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"merge_file_modifier": "data",
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"feature_file_modifier": "features",
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"matrix_file_modifier": "matrix",
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"predict_file_modifier": "predictions", # predict, predict-rolling
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"signal_file_modifier": "performance",
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# =====================================
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# === MERGER and (online) COLLECTOR ===
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# Specify which data sources to merge into one file with all source columns
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# Format: (folder, file, prefix) - (symbol, data source, column prefix)
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# These descriptors are also used to retrieve data by collector in online mode - folder name is symbol name
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#"data_sources": [
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# {"folder": "BTCUSDT", "file": "klines", "column_prefix": ""},
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#],
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#"data_sources": [
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# {"folder": "BTCUSDT", "file": "klines", "column_prefix": "btc"},
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# {"folder": "ETHUSDT", "file": "klines", "column_prefix": "eth"},
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#],
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# ==========================
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# === FEATURE GENERATION ===
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# What columns to pass to which feature generator and how to prefix its derived features
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"feature_sets": [
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{"column_prefix": "btc", "generator": "klines", "feature_prefix": "btc"},
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{"column_prefix": "eth", "generator": "klines", "feature_prefix": "eth"},
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],
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# Parameters of klines feature generator
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# If these are changed, then feature names (below) will also have to be changed
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#"base_window_kline": 40320, # 4 weeks
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#"windows_kline": [1, 20, 60, 360, 1440, 10080],
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#"area_windows_kline": [20, 60, 360, 1440, 10080],
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#"base_window_kline": 20160, # 2 weeks
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#"windows_kline": [1, 20, 60, 180, 720, 2880],
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#"area_windows_kline": [60, 120, 180, 720, 2880],
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#"windows_kline": [1, 30, 120, 360, 1440, 4320],
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#"area_windows_kline": [30, 120, 360, 1440, 4320],
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#"windows_kline": [1, 60, 180, 720, 2880, 5760],
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#"area_windows_kline": [60, 180, 720, 2880, 5760],
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"base_window_kline": 10080, # 1 week
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"windows_kline": [1, 10, 30, 180, 720, 1440],
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"area_windows_kline": [10, 30, 180, 720, 1440],
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# ========================
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# === LABEL GENERATION ===
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"high_low_horizon": 1440, # Parameter of labels: computing max and min for this horizon ahead
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"top_bot_column_name": "close",
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# ===========================
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# === MODEL TRAIN/PREDICT ===
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# off-line and on-line
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# Minimum history required to compute derived features
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# It is used in online mode where we need to maintain data window of this size or larger
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# Take maximum aggregation windows from feature generation code (and add something to be sure that we have all what is needed)
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# Basically, should be equal to base_window_kline
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"features_horizon": 10180,
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# One model is for each (label, train_features, algorithm)
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# Feature column names returned by the klines feature generator
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# They are used by train/predict
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"features_kline": [
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'btc_close_1', 'btc_close_10', 'btc_close_30', 'btc_close_180', 'btc_close_720', 'btc_close_1440',
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'btc_close_std_10', 'btc_close_std_30', 'btc_close_std_180', 'btc_close_std_720', 'btc_close_std_1440',
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'btc_volume_1', 'btc_volume_10', 'btc_volume_30', 'btc_volume_180', 'btc_volume_720', 'btc_volume_1440',
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'btc_span_1', 'btc_span_10', 'btc_span_30', 'btc_span_180', 'btc_span_720', 'btc_span_1440',
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'btc_trades_1', 'btc_trades_10', 'btc_trades_30', 'btc_trades_180', 'btc_trades_720', 'btc_trades_1440',
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'btc_tb_base_1', 'btc_tb_base_10', 'btc_tb_base_30', 'btc_tb_base_180', 'btc_tb_base_720', 'btc_tb_base_1440',
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#'btc_close_area_10', 'btc_close_area_30', 'btc_close_area_180', 'btc_close_area_720', 'btc_close_area_1440',
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'btc_close_trend_10', 'btc_close_trend_30', 'btc_close_trend_180', 'btc_close_trend_720', 'btc_close_trend_1440',
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'btc_volume_trend_10', 'btc_volume_trend_30', 'btc_volume_trend_180', 'btc_volume_trend_720', 'btc_volume_trend_1440',
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'eth_close_1', 'eth_close_10', 'eth_close_30', 'eth_close_180', 'eth_close_720', 'eth_close_1440',
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'eth_close_std_10', 'eth_close_std_30', 'eth_close_std_180', 'eth_close_std_720', 'eth_close_std_1440',
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'eth_volume_1', 'eth_volume_10', 'eth_volume_30', 'eth_volume_180', 'eth_volume_720', 'eth_volume_1440',
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'eth_span_1', 'eth_span_10', 'eth_span_30', 'eth_span_180', 'eth_span_720', 'eth_span_1440',
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'eth_trades_1', 'eth_trades_10', 'eth_trades_30', 'eth_trades_180', 'eth_trades_720', 'eth_trades_1440',
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'eth_tb_base_1', 'eth_tb_base_10', 'eth_tb_base_30', 'eth_tb_base_180', 'eth_tb_base_720', 'eth_tb_base_1440',
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#'eth_close_area_10', 'eth_close_area_30', 'eth_close_area_180', 'eth_close_area_720', 'eth_close_area_1440',
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'eth_close_trend_10', 'eth_close_trend_30', 'eth_close_trend_180', 'eth_close_trend_720', 'eth_close_trend_1440',
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'eth_volume_trend_10', 'eth_volume_trend_30', 'eth_volume_trend_180', 'eth_volume_trend_720', 'eth_volume_trend_1440'
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],
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# Used to select columns for training by adding together different feature sets (the lists are defined elswhere in variables like "features_kline")
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"train_features": ["kline"],
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# algorithm descriptors from model store
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"algorithms": ["nn"], # gb, nn, lc - definitions of their parameters are in model store
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# This will be excluded from model training
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"label_horizon": 0,
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# These labels are specified manually and are produced by label generator
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# Models will be trained for these models
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"labels": [
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"bot2_025", "bot2_05", "bot2_075", "bot2_1", "bot2_125", "bot2_15",
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"top2_025", "top2_05", "top2_075", "top2_1", "top2_125", "top2_15",
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],
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"_labels": [
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"bot3_025", "bot3_05", "bot3_075", "bot3_1", "bot3_125", "bot3_15", "bot3_175",
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"top3_025", "top3_05", "top3_075", "top3_1", "top3_125", "top3_15", "top3_175",
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"bot4_025", "bot4_05", "bot4_075", "bot4_1", "bot4_125", "bot4_15", "bot4_175", "bot4_2",
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"top4_025", "top4_05", "top4_075", "top4_1", "top4_125", "top4_15", "top4_175", "top4_2",
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"bot5_025", "bot5_05", "bot5_075", "bot5_1", "bot5_125", "bot5_15", "bot5_175", "bot5_2", "bot5_25",
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"top5_025", "top5_05", "top5_075", "top5_1", "top5_125", "top5_15", "top5_175", "top5_2", "top5_25",
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'high_max_180', # Maximum high (relative)
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"high_10", "high_15", "high_20", "high_25", "high_30",
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"low_10", "low_15", "low_20", "low_25", "low_30",
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'low_min_180', # Minimum low (relative)
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'low_01', 'low_02', 'low_03', 'low_04', # Always above
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'low_10', 'low_15', 'low_20', 'low_25', # At least one time below
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'high_to_low_180',
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'close_area_future_60', 'close_area_future_120', 'close_area_future_180', 'close_area_future_300',
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],
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# =========================
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# === SIGNAL GENERATION ===
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# These are predicted columns <label, train_features, algorithm> as well as model (pair) names
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"buy_labels": [],
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"sell_labels": [],
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"_buy_labels": ["bot4_1_k_lc", "bot4_15_k_lc", "bot4_2_k_lc", "bot4_25_k_lc", "bot4_3_k_lc"],
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"_sell_labels": ["top4_1_k_lc", "top4_15_k_lc", "top4_2_k_lc", "top4_25_k_lc", "top4_3_k_lc"],
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# It defines how signal scores, trade signals, and notification signals will be generated
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# from point-wise prediction scores for two groups of labels
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"signal_model": {
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# First, aggregation in group over various algorithms and label parameters
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"buy_point_threshold": None, # Second, produce boolean column (optional)
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"buy_window": 3, # Third, aggregate in time
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# Now we have the final score
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"buy_signal_threshold": 0.65, # To decide whether to buy/sell after all aggregations/combinations
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"buy_notify_threshold": 0.05, # To decide whether to notify (can be an option of individual users/consumers)
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"combine": "", # "no_combine", "relative", "difference" Find relative/difference
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"sell_point_threshold": None,
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"sell_window": 3,
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"sell_signal_threshold": 0.65,
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"sell_notify_threshold": 0.05,
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"trade_icon_step": 0.1, # For each step, one icon added
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"notify_frequency_minutes": 10, # 1m, 5m, 10m, 15m etc. Minutes will be divided by this number
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},
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# =====================
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# === TRADER SERVER ===
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"base_asset": "", # BTC ETH
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"quote_asset": "",
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"trader": {
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# For debugging: determine what parts of code will be executed
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"no_trades_only_data_processing": False, # in market or out of market processing is excluded (all below parameters ignored)
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"test_order_before_submit": False, # Send test submit to the server as part of validation
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"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)
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"percentage_used_for_trade": 99, # in % to the available USDT quantity, that is, we will derive how much BTC to buy using this percentage
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"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)
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# Signal model (trade strategy) - currently NOT USED
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"sell_timeout": 70, # Seconds
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"percentage_sell_price": 1.018, # our planned profit per trade via limit sell order (part of the model)
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},
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# ==================
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# === COLLECTORS ===
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"collector": {
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"folder": "DATA",
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"flush_period": 300, # seconds
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"depth": {
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"folder": "DEPTH",
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"symbols": ["BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH"],
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"limit": 100, # Legal values (depth): '5, 10, 20, 50, 100, 500, 1000, 5000' <100 weight=1
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"freq": "1m", # Binance standard frequency: 5s, 1m etc.
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},
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"stream": {
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"folder": "STREAM",
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# Stream formats:
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# For kline channel: <symbol>@kline_<interval>, Event type: "e": "kline", Symbol: "s": "BNBBTC"
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# For depth channel: <symbol>@depth<levels>[@100ms], Event type: NO, Symbol: NO
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# btcusdt@ticker
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"channels": ["kline_1m", "depth20"], # kline_1m, depth20, depth5
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"symbols": ["BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH"],
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# "BTCUSDT", "ETHBTC", "ETHUSDT", "IOTAUSDT", "IOTABTC", "IOTAETH"
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}
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},
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}
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def data_provider_problems_exist():
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if App.error_status != 0:
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return True
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if App.server_status != 0:
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return True
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return False
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def problems_exist():
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if App.error_status != 0:
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return True
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if App.server_status != 0:
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return True
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if App.account_status != 0:
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return True
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if App.trade_state_status != 0:
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return True
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return False
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def load_config(config_file):
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if config_file:
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config_file_path = PACKAGE_ROOT / config_file
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with open(config_file_path) as json_file:
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config_json = json.load(json_file)
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App.config.update(config_json)
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if __name__ == "__main__":
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pass
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