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298 lines
12 KiB
Python
298 lines
12 KiB
Python
"""
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Functions to convert orderflow data from public_trades
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"""
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import logging
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import time
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import typing
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from collections import OrderedDict
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from datetime import datetime
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from typing import Tuple
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import numpy as np
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import pandas as pd
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from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS
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from freqtrade.enums import RunMode
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from freqtrade.exceptions import DependencyException
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logger = logging.getLogger(__name__)
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def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame):
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"""
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Populates a dataframe with trades columns
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:param dataframe: Dataframe to populate
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"""
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# Initialize columns with appropriate dtypes
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dataframe["trades"] = np.nan
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dataframe["orderflow"] = np.nan
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dataframe["imbalances"] = np.nan
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dataframe["stacked_imbalances_bid"] = np.nan
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dataframe["stacked_imbalances_ask"] = np.nan
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dataframe["max_delta"] = np.nan
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dataframe["min_delta"] = np.nan
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dataframe["bid"] = np.nan
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dataframe["ask"] = np.nan
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dataframe["delta"] = np.nan
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dataframe["total_trades"] = np.nan
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# Ensure the 'trades' column is of object type
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dataframe["trades"] = dataframe["trades"].astype(object)
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dataframe["orderflow"] = dataframe["orderflow"].astype(object)
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dataframe["imbalances"] = dataframe["imbalances"].astype(object)
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dataframe["stacked_imbalances_bid"] = dataframe["stacked_imbalances_bid"].astype(object)
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dataframe["stacked_imbalances_ask"] = dataframe["stacked_imbalances_ask"].astype(object)
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def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
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from freqtrade.exchange import timeframe_to_next_date, timeframe_to_resample_freq
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timeframe_frequency = timeframe_to_resample_freq(timeframe)
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# calculate ohlcv candle start and end
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if df is not None and not df.empty:
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df["datetime"] = pd.to_datetime(df["date"], unit="ms")
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df["candle_start"] = df["datetime"].dt.floor(timeframe_frequency)
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# used in _now_is_time_to_refresh_trades
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df["candle_end"] = df["candle_start"].apply(
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lambda candle_start: timeframe_to_next_date(timeframe, candle_start)
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)
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df.drop(columns=["datetime"], inplace=True)
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def populate_dataframe_with_trades(
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cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], pd.DataFrame],
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config,
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dataframe: pd.DataFrame,
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trades: pd.DataFrame,
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) -> Tuple[pd.DataFrame, OrderedDict[Tuple[datetime, datetime], pd.DataFrame]]:
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"""
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Populates a dataframe with trades
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:param dataframe: Dataframe to populate
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:param trades: Trades to populate with
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:return: Dataframe with trades populated
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"""
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timeframe = config["timeframe"]
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config_orderflow = config["orderflow"]
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# create columns for trades
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_init_dataframe_with_trades_columns(dataframe)
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if trades is None or trades.empty:
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return dataframe, cached_grouped_trades
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try:
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start_time = time.time()
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# calculate ohlcv candle start and end
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_calculate_ohlcv_candle_start_and_end(trades, timeframe)
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# get date of earliest max_candles candle
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max_candles = config_orderflow["max_candles"]
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start_date = dataframe.tail(max_candles).date.iat[0]
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# slice of trades that are before current ohlcv candles to make groupby faster
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trades = trades.loc[trades["candle_start"] >= start_date]
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trades.reset_index(inplace=True, drop=True)
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# group trades by candle start
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trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False)
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# Create Series to hold complex data
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trades_series = pd.Series(index=dataframe.index, dtype=object)
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orderflow_series = pd.Series(index=dataframe.index, dtype=object)
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imbalances_series = pd.Series(index=dataframe.index, dtype=object)
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stacked_imbalances_bid_series = pd.Series(index=dataframe.index, dtype=object)
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stacked_imbalances_ask_series = pd.Series(index=dataframe.index, dtype=object)
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trades_grouped_by_candle_start = trades.groupby("candle_start", group_keys=False)
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for candle_start, trades_grouped_df in trades_grouped_by_candle_start:
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is_between = candle_start == dataframe["date"]
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if is_between.any():
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from freqtrade.exchange import timeframe_to_next_date
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candle_next = timeframe_to_next_date(timeframe, typing.cast(datetime, candle_start))
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if candle_next not in trades_grouped_by_candle_start.groups:
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logger.warning(
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f"candle at {candle_start} with {len(trades_grouped_df)} trades "
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f"might be unfinished, because no finished trades at {candle_next}"
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)
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indices = dataframe.index[is_between].tolist()
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# Add trades to each candle
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trades_series.loc[indices] = [
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trades_grouped_df.drop(columns=["candle_start", "candle_end"]).to_dict(
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orient="records"
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)
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]
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# Use caching mechanism
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if (candle_start, candle_next) in cached_grouped_trades:
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cache_entry = cached_grouped_trades[
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(typing.cast(datetime, candle_start), candle_next)
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]
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# dataframe.loc[is_between] = cache_entry # doesn't take, so we need workaround:
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# Create a dictionary of the column values to be assigned
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update_dict = {c: cache_entry[c].iat[0] for c in cache_entry.columns}
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# Assign the values using the update_dict
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dataframe.loc[is_between, update_dict.keys()] = pd.DataFrame(
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[update_dict], index=dataframe.loc[is_between].index
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)
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continue
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# Calculate orderflow for each candle
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orderflow = trades_to_volumeprofile_with_total_delta_bid_ask(
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trades_grouped_df, scale=config_orderflow["scale"]
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)
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orderflow_series.loc[indices] = [orderflow.to_dict(orient="index")]
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# Calculate imbalances for each candle's orderflow
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imbalances = trades_orderflow_to_imbalances(
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orderflow,
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imbalance_ratio=config_orderflow["imbalance_ratio"],
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imbalance_volume=config_orderflow["imbalance_volume"],
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)
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imbalances_series.loc[indices] = [imbalances.to_dict(orient="index")]
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stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
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stacked_imbalances_bid_series.loc[indices] = [
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stacked_imbalance_bid(
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imbalances, stacked_imbalance_range=stacked_imbalance_range
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)
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]
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stacked_imbalances_ask_series.loc[indices] = [
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stacked_imbalance_ask(
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imbalances, stacked_imbalance_range=stacked_imbalance_range
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)
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]
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bid = np.where(
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trades_grouped_df["side"].str.contains("sell"), trades_grouped_df["amount"], 0
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)
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ask = np.where(
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trades_grouped_df["side"].str.contains("buy"), trades_grouped_df["amount"], 0
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)
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deltas_per_trade = ask - bid
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min_delta = deltas_per_trade.cumsum().min()
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max_delta = deltas_per_trade.cumsum().max()
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dataframe.loc[indices, "max_delta"] = max_delta
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dataframe.loc[indices, "min_delta"] = min_delta
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dataframe.loc[indices, "bid"] = bid.sum()
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dataframe.loc[indices, "ask"] = ask.sum()
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dataframe.loc[indices, "delta"] = (
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dataframe.loc[indices, "ask"] - dataframe.loc[indices, "bid"]
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)
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dataframe.loc[indices, "total_trades"] = len(trades_grouped_df)
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# Cache the result
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cached_grouped_trades[(typing.cast(datetime, candle_start), candle_next)] = (
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dataframe.loc[is_between].copy()
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)
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# Maintain cache size
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if (
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config.get("runmode") in (RunMode.DRY_RUN, RunMode.LIVE)
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and len(cached_grouped_trades) > config_orderflow["cache_size"]
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):
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cached_grouped_trades.popitem(last=False)
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else:
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logger.debug(f"Found NO candles for trades starting with {candle_start}")
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logger.debug(f"trades.groups_keys in {time.time() - start_time} seconds")
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# Merge the complex data Series back into the DataFrame
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dataframe["trades"] = trades_series
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dataframe["orderflow"] = orderflow_series
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dataframe["imbalances"] = imbalances_series
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dataframe["stacked_imbalances_bid"] = stacked_imbalances_bid_series
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dataframe["stacked_imbalances_ask"] = stacked_imbalances_ask_series
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except Exception as e:
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logger.exception("Error populating dataframe with trades")
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raise DependencyException(e)
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return dataframe, cached_grouped_trades
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def trades_to_volumeprofile_with_total_delta_bid_ask(
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trades: pd.DataFrame, scale: float
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) -> pd.DataFrame:
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"""
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:param trades: dataframe
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:param scale: scale aka bin size e.g. 0.5
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:return: trades binned to levels according to scale aka orderflow
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"""
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df = pd.DataFrame([], columns=DEFAULT_ORDERFLOW_COLUMNS)
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# create bid, ask where side is sell or buy
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df["bid_amount"] = np.where(trades["side"].str.contains("sell"), trades["amount"], 0)
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df["ask_amount"] = np.where(trades["side"].str.contains("buy"), trades["amount"], 0)
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df["bid"] = np.where(trades["side"].str.contains("sell"), 1, 0)
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df["ask"] = np.where(trades["side"].str.contains("buy"), 1, 0)
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# round the prices to the nearest multiple of the scale
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df["price"] = ((trades["price"] / scale).round() * scale).astype("float64").values
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if df.empty:
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df["total"] = np.nan
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df["delta"] = np.nan
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return df
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df["delta"] = df["ask_amount"] - df["bid_amount"]
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df["total_volume"] = df["ask_amount"] + df["bid_amount"]
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df["total_trades"] = df["ask"] + df["bid"]
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# group to bins aka apply scale
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df = df.groupby("price").sum(numeric_only=True)
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return df
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def trades_orderflow_to_imbalances(df: pd.DataFrame, imbalance_ratio: int, imbalance_volume: int):
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"""
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:param df: dataframes with bid and ask
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:param imbalance_ratio: imbalance_ratio e.g. 3
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:param imbalance_volume: imbalance volume e.g. 10
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:return: dataframe with bid and ask imbalance
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"""
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bid = df.bid
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# compares bid and ask diagonally
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ask = df.ask.shift(-1)
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bid_imbalance = (bid / ask) > (imbalance_ratio)
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# overwrite bid_imbalance with False if volume is not big enough
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bid_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, bid_imbalance)
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ask_imbalance = (ask / bid) > (imbalance_ratio)
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# overwrite ask_imbalance with False if volume is not big enough
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ask_imbalance_filtered = np.where(df.total_volume < imbalance_volume, False, ask_imbalance)
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dataframe = pd.DataFrame(
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{"bid_imbalance": bid_imbalance_filtered, "ask_imbalance": ask_imbalance_filtered},
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index=df.index,
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)
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return dataframe
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def stacked_imbalance(
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df: pd.DataFrame, label: str, stacked_imbalance_range: int, should_reverse: bool
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):
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"""
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y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
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https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
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"""
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imbalance = df[f"{label}_imbalance"]
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int_series = pd.Series(np.where(imbalance, 1, 0))
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stacked = int_series * (
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int_series.groupby((int_series != int_series.shift()).cumsum()).cumcount() + 1
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)
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max_stacked_imbalance_idx = stacked.index[stacked >= stacked_imbalance_range]
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stacked_imbalance_price = np.nan
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if not max_stacked_imbalance_idx.empty:
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idx = (
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max_stacked_imbalance_idx[0]
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if not should_reverse
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else np.flipud(max_stacked_imbalance_idx)[0]
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)
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stacked_imbalance_price = imbalance.index[idx]
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return stacked_imbalance_price
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def stacked_imbalance_ask(df: pd.DataFrame, stacked_imbalance_range: int):
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return stacked_imbalance(df, "ask", stacked_imbalance_range, should_reverse=True)
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def stacked_imbalance_bid(df: pd.DataFrame, stacked_imbalance_range: int):
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return stacked_imbalance(df, "bid", stacked_imbalance_range, should_reverse=False)
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