intelligent-trading-bot/service/analyzer.py

from pathlib import Path
from typing import Union
import json
import pickle
from datetime import datetime, date, timedelta
import queue

import numpy as np
import pandas as pd

from service.App import *
from common.utils import *
from common.classifiers import *
from common.feature_generation import *
from common.signal_generation import *

import logging
log = logging.getLogger('analyzer')


class Analyzer:
    """
    In-memory database which represents the current state of the (trading) environment including its history.

    Properties of klines:
    - "timestamp" is a left border of the interval like "2017-08-17 04:00:00"
    - "close_time" is a right border of the interval in ms (last millisecond) like "1502942459999" equivalent to "2017-08-17 04:00::59.999"
    """

    def __init__(self, config):
        """
        Create a new operation object using its definition.

        :param config: Initialization parameters defining what is in the database including its persistent parameters and schema
        """

        self.config = config

        #
        # Data state
        #

        # Klines are stored as a dict of lists. Key is a symbol and the list is a list of latest kline records
        # One kline record is a list of values (not dict) as returned by API: open time, open, high, low, close, volume etc.
        self.klines = {}

        self.queue = queue.Queue()

        #
        # Load models
        #
        model_path = App.config["model_folder"]
        model_path = Path(model_path)
        if not model_path.is_absolute():
            model_path = PACKAGE_ROOT / model_path
        model_path = model_path.resolve()

        labels = App.config["labels"]
        feature_sets = ["kline"]
        algorithms = ["gb", "nn", "lc"]
        self.models = load_models(model_path, labels, feature_sets, algorithms)

        #
        # Start a thread for storing data
        #

    #
    # Data state operations
    #

    def get_klines_count(self, symbol):
        return len(self.klines.get(symbol, []))

    def get_last_kline(self, symbol):
        if self.get_klines_count(symbol) > 0:
            return self.klines.get(symbol)[-1]
        else:
            return None

    def get_last_kline_ts(self, symbol):
        """Open time of the last kline. It is simultaneously kline id. Add 1m if the end is needed."""
        last_kline = self.get_last_kline(symbol=symbol)
        if not last_kline:
            return 0
        last_kline_ts = last_kline[0]
        return last_kline_ts

    def get_missing_klines_count(self, symbol):
        now_ts = now_timestamp()
        last_kline_ts = self.get_last_kline_ts(symbol)
        if not last_kline_ts:
            return App.config["signaler"]["analysis"]["features_horizon"]
        end_of_last_kline = last_kline_ts + 60_000  # Plus 1m

        minutes = (now_ts - end_of_last_kline) / 60_000
        minutes += 2
        return int(minutes)

    def store_klines(self, data: dict):
        """
        Store latest klines for the specified symbols.
        Existing klines for the symbol and timestamp will be overwritten.

        :param data: Dict of lists with symbol as a key, and list of klines for this symbol as a value.
            Example: { 'BTCUSDT': [ [], [], [] ] }
        :type dict:
        """
        now_ts = now_timestamp()

        for symbol, klines in data.items():
            # If symbol does not exist then create
            klines_data = self.klines.get(symbol)
            if klines_data is None:
                self.klines[symbol] = []
                klines_data = self.klines.get(symbol)

            ts = klines[0][0]  # Very first timestamp of the new data

            # Find kline with this or younger timestamp in the database
            # same_kline = next((x for x in klines_data if x[0] == ts), None)
            existing_indexes = [i for i, x in enumerate(klines_data) if x[0] >= ts]
            #print(f"===>>> Existing tss: {[x[0] for x in klines_data]}")
            #print(f"===>>> New tss: {[x[0] for x in klines]}")
            #print(f"===>>> {symbol} Overlap {len(existing_indexes)}. Existing Indexes: {existing_indexes}")
            if existing_indexes:  # If there is overlap with new klines
                start = min(existing_indexes)
                num_deleted = len(klines_data) - start
                del klines_data[start:]  # Delete starting from the first kline in new data (which will be added below)
                if len(klines) < num_deleted:  # It is expected that we add same or more klines than deleted
                    log.error("More klines is deleted by new klines added, than we actually add. Something woring with timestamps and storage logic.")

            # Append new klines
            klines_data.extend(klines)

            # Remove too old klines
            kline_window = App.config["signaler"]["analysis"]["features_horizon"]
            to_delete = len(klines_data) - kline_window
            if to_delete > 0:
                del klines_data[:to_delete]

            # Check validity. It has to be an ordered time series with certain frequency
            for i, kline in enumerate(self.klines.get(symbol)):
                ts = kline[0]
                if i > 0:
                    if ts - prev_ts != 60_000:
                        log.error("Wrong sequence of klines. They are expected to be a regular time series with 1m frequency.")
                prev_ts = kline[0]

            # Debug message about the last received kline end and current ts (which must be less than 1m - rather small delay)
            log.debug(f"Stored klines. Total {len(klines_data)} in db. Last kline end: {self.get_last_kline_ts(symbol)+60_000}. Current time: {now_ts}")

    def store_depth(self, depths: list, freq):
        """
        Persistently store order books from the input list. Each entry is one response from order book request for one symbol.
        Currently the order books are directly stored in a file (for this symbol) and not in this object.

        :param depths: List of dicts where each dict is an order book with such fields as 'asks', 'bids' and 'symbol' (symbol is added after loading).
        :type list:
        """

        # File name like TRADE_HOME/COLLECT/DEPTH/depth-BTCUSDT-5s.txt
        TRADE_DATA = "."  # TODO: We need to read it from the environment. It could be data dir or docker volume.
        # BASE_DIR = Path(__file__).resolve().parent.parent
        # BASE_DIR = Path.cwd()

        for depth in depths:
            # TODO: The result might be an exception or some other object denoting bad return (timeout, cancelled etc.)

            symbol = depth["symbol"]

            path = Path(TRADE_DATA).joinpath(App.config["collector"]["folder"])
            path = path.joinpath(App.config["collector"]["depth"]["folder"])
            path.mkdir(parents=True, exist_ok=True)  # Ensure that dir exists

            file_name = f"depth-{symbol}-{freq}"
            file = Path(path, file_name).with_suffix(".txt")

            # Append to the file (create if it does not exist)
            json_line = json.dumps(depth)
            with open(file, 'a+') as f:
                f.write(json_line + "\n")

    def store_queue(self):
        """
        Persistently store the queue data to one or more files corresponding to the stream (event) type, symbol (and frequency).

        :return:
        """
        #
        # Get all the data from the queue
        #
        events = {}
        item = None
        while True:
            try:
                item = self.queue.get_nowait()
            except queue.Empty as ee:
                break
            except:
                break

            if item is None:
                break

            c = item.get("e")  # Channel
            if not events.get(c):  # Insert if does not exit
                events[c] = {}
            symbols = events[c]

            s = item.get("s")  # Symbol
            if not symbols.get(s):  # Insert if does not exit
                symbols[s] = []
            data = symbols[s]

            data.append(item)

            self.queue.task_done()  # TODO: Do we really need this?

        # File name like TRADE_HOME/COLLECT/DEPTH/depth-BTCUSDT-5s.txt
        TRADE_DATA = "."  # TODO: We need to read it from the environment. It could be data dir or docker volume.
        # BASE_DIR = Path(__file__).resolve().parent.parent
        # BASE_DIR = Path.cwd()

        path = Path(TRADE_DATA).joinpath(App.config["collector"]["folder"])
        path = path.joinpath(App.config["collector"]["stream"]["folder"])
        path.mkdir(parents=True, exist_ok=True)  # Ensure that dir exists

        now = datetime.utcnow()
        #rotate_suffix = f"{now:%Y}{now:%m}{now:%d}"  # Daily files
        rotate_suffix = f"{now:%Y}{now:%m}"  # Monthly files

        #
        # Get all the data from the queue and store in file
        #
        for c, symbols in events.items():
            for s, data in symbols.items():
                file_name = f"{c}-{s}-{rotate_suffix}"
                file = Path(path, file_name).with_suffix(".txt")

                # Append to the file (create if it does not exist)
                data = [json.dumps(event) for event in data]
                data_str = "\n".join(data)
                with open(file, 'a+') as f:
                    f.write(data_str + "\n")

    #
    # Analysis (features, predictions, signals etc.)
    #

    def analyze(self):
        """
        1. Convert klines to df
        2. Derive (compute) features (use same function as for model training)
        3. Derive (predict) labels by applying models trained for each label
        4. Generate buy/sell signals by applying rule models trained for best overall trade performance
        """
        symbol = App.config["symbol"]

        klines = self.klines.get(symbol)
        last_kline_ts = self.get_last_kline_ts(symbol)

        log.info(f"Analyze {symbol}. {len(klines)} klines in the database. Last kline timestamp: {last_kline_ts}")

        #
        # 1.
        # Produce a data frame with înput data
        #
        try:
            df = klines_to_df(klines)
        except Exception as e:
            print(f"Error in klines_to_df: {e}")
            return

        #
        # 2.
        # Generate all necessary derived features (NaNs are possible due to short history)
        #
        try:
            features_out = generate_features(
                df, use_differences=False,
                base_window=App.config["base_window_kline"], windows=App.config["windows_kline"],
                area_windows=App.config["area_windows_kline"]
            )
        except Exception as e:
            print(f"Error in generate_features: {e}")
            return

        # Now we have as many additional columns as we have defined derived features

        #
        # 3.
        # Generate scores using existing models (trained in advance using latest history by a separate script)
        #

        # kline feature set
        features = App.config["features_kline"]
        predict_df = df[features]
        # Do not drop nans because they will be processed by predictor

        # Do prediction by applying models to the data
        score_df = pd.DataFrame(index=predict_df.index)
        try:
            for score_column_name, model_pair in self.models.items():
                if score_column_name.endswith("_gb"):
                    df_y_hat = predict_gb(model_pair, predict_df)
                elif score_column_name.endswith("_nn"):
                    df_y_hat = predict_nn(model_pair, predict_df)
                elif score_column_name.endswith("_lc"):
                    df_y_hat = predict_lc(model_pair, predict_df)
                else:
                    raise ValueError(f"Unknown column name algorithm suffix {score_column_name[-3:]}. Currently only '_gb', '_nn', '_lc' are supported.")
                score_df[score_column_name] = df_y_hat
        except Exception as e:
            print(f"Error in predict: {e}")
            return

        # Now we have all predictions (score columns) needed to make a buy/sell decision - many predictions for each true label column
        # We will need only the latest row for signal generation

        #
        # 4.
        # Generate buy/sell signals using rules and thresholds
        #
        all_scores = self.models.keys()
        high_scores = [col for col in all_scores if "high_" in col]  # 3 algos x 3 thresholds x 1 k = 9
        low_scores = [col for col in all_scores if "low_" in col]  # 3 algos x 3 thresholds x 1 k = 9

        # Compute final score column
        score_df["high"] = score_df[high_scores].mean(axis=1)
        score_df["low"] = score_df[low_scores].mean(axis=1)
        high_and_low = score_df["high"] + score_df["low"]
        score_df["score"] = ((score_df["high"] / high_and_low) * 2) - 1.0  # in [-1, +1]

        score = score_df.iloc[-1].score

        row = df.iloc[-1]
        close_price = row.close
        high_price = row.high
        low_price = row.high
        timestamp = row.name
        close_time = row.name+timedelta(minutes=1)  # row.close_time

        # Thresholds
        model = App.config["signaler"]["model"]

        signal = dict(side=None, score=score, close_price=close_price, close_time=close_time)
        if not score:
            signal = dict()
        elif score > model.get("buy_threshold"):
            signal["side"] = "BUY"
        elif score < model.get("sell_threshold"):
            signal["side"] = "SELL"
        else:
            signal["side"] = ""

        App.signal = signal

        log.info(f"Analyze finished. Score: {score:+.2f}. Signal: {signal['side']}. Price: {int(close_price):,}")


if __name__ == "__main__":
    pass