252 lines
11 KiB
Python
252 lines
11 KiB
Python
from pathlib import Path
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import click
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from tqdm import tqdm
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import numpy as np
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import pandas as pd
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from sklearn.metrics import (precision_recall_curve, PrecisionRecallDisplay, RocCurveDisplay)
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from sklearn.model_selection import ParameterGrid
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from service.App import *
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from common.classifiers import *
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from common.label_generation_topbot import *
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from common.signal_generation import *
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"""
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Input data:
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This script assumes the existence of label prediction scores for a list of labels
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which is computed by some other script (train predict models or (better) rolling predictions).
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It also uses the real prices in order to determine if the orders are executed or not
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(currently close prices but it is better to use high and low prices).
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Purpose:
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The script uses some signal parameters which determine whether to sell or buy based on the current
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label prediction scores. It simulates trade for such signal parameters by running through
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the whole data set. For each such signal parameters, it determines the trade performance
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(overall profit or loss). It then does such simulations for all defined signal parameters
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and finally chooses the best performing parameters. These parameters can be then used for real trades.
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Notes:
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- The simulation is based on some aggregation function which computes the final signal from
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multiple label prediction scores. There could be different aggregation logics for example
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finding average value or using pre-defined thresholds or even training some kind of model
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like decision trees
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- The signal (aggregation) function assumes that there two kinds of labels: positive (indicating that
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the price will go up) and negative (indicating that the price will go down). The are accordingly
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stored in two lists in the configuration
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- Tthe script should work with both batch predictions and (better) rolling predictions by
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assuming only the necessary columns for predicted label scores and trade columns (close price)
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"""
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class P:
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in_nrows = 100_000_000
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start_index = 0 # 200_000 for 1m btc
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end_index = None
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# True if buy and sell hyper-parameters are equal
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# Only buy parameters will be used and sell parameters will be ignored
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buy_sell_equal = False
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# Haw many best performing parameters from the grid to store
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topn_to_store = 10
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#
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# Specify the ranges of signal hyper-parameters
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#
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grid_signals = [
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{
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"buy_point_threshold": [None], # np.arange(0.01, 0.21, 0.01).tolist(), # None means do not use
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"buy_window": [5],
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"buy_signal_threshold": np.arange(0.15, 0.30, 0.01).tolist(),
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"buy_slope_threshold": [None],
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# If two groups are equal, then these values are ignored
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"sell_point_threshold": [None],
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"sell_window": [5],
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"sell_signal_threshold": np.arange(0.15, 0.30, 0.01).tolist(),
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"sell_slope_threshold": [None],
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"combine": ["no_combine"], # "no_combine", "difference" (same as no combine or better), "relative" (rather bad)
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},
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]
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@click.command()
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@click.option('--config_file', '-c', type=click.Path(), default='', help='Configuration file name')
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def main(config_file):
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"""
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The goal is to find how good interval scores can be by performing grid search through
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all aggregation/patience hyper-parameters which generate buy-sell signals on interval level.
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Here we measure performance of trade using top-bottom scores generated using specified aggregation
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parameters (which are searched through a grid). Here lables with true records are not needed.
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In contrast, in another (above) function we do the same search but measure interval-score,
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that is, how many intervals are true and false (either bot or bottom) by comparing with true label.
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General purpose and assumptions. Load any file with two groups of point-wise prediction scores:
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buy score and sell columns. The file must also have columns for trade simulation like close price.
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It can be batch prediction file (one train model and one prediction result) or rolling predictions
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(multiple sequential trains and predictions).
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The script will convert these two buy-sell column groups to boolean buy-sell signals by using
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signal generation hyper-parameters, and then apply trade simulation by computing its overall
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performance. This is done for all simulation parameters from the grid. The results for all
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simulation parameters and their performance are stored in the output file.
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"""
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load_config(config_file)
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time_column = App.config["time_column"]
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now = datetime.now()
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symbol = App.config["symbol"]
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data_path = Path(App.config["data_folder"]) / symbol
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if not data_path.is_dir():
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print(f"Data folder does not exist: {data_path}")
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return
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out_path = Path(App.config["data_folder"]) / symbol
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out_path.mkdir(parents=True, exist_ok=True) # Ensure that folder exists
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#
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# Load data with (rolling) label point-wise predictions
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#
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file_path = (data_path / App.config.get("predict_file_name")).with_suffix(".csv")
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if not file_path.exists():
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print(f"ERROR: Input file does not exist: {file_path}")
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return
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print(f"Loading predictions from input file: {file_path}")
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df = pd.read_csv(file_path, parse_dates=[time_column], nrows=P.in_nrows)
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print(f"Predictions loaded. Length: {len(df)}. Width: {len(df.columns)}")
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# Limit size according to parameters start_index end_index
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df = df.iloc[P.start_index:P.end_index]
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df = df.reset_index(drop=True)
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#
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# Find maximum performance possible based on true labels only
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#
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# Best parameters (just to compute for known parameters)
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#df['buy_signal_column'] = score_to_signal(df[bot_score_column], None, 5, 0.09)
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#df['sell_signal_column'] = score_to_signal(df[top_score_column], None, 10, 0.064)
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#performance_long, performance_short, long_count, short_count, long_profitable, short_profitable, longs, shorts = performance_score(df, 'sell_signal_column', 'buy_signal_column', 'close')
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# TODO: Save maximum performance in output file or print it (use as a reference)
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# Maximum possible on labels themselves
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#performance_long, performance_short, long_count, short_count, long_profitable, short_profitable, longs, shorts = performance_score(df, 'top10_2', 'bot10_2', 'close')
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#
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# Optimization: Compute averages which will be the same for all hyper-parameters
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#
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buy_labels = App.config["buy_labels"]
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sell_labels = App.config["sell_labels"]
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if set(buy_labels + sell_labels) - set(df.columns):
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missing_labels = list(set(buy_labels + sell_labels) - set(df.columns))
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print(f"ERROR: Some buy/sell labels from config are not present in the input data. Missing labels: {missing_labels}")
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return
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buy_score_column_avg = 'buy_score_column_avg'
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sell_score_column_avg = 'sell_score_column_avg'
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df[buy_score_column_avg] = df[buy_labels].mean(skipna=True, axis=1)
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df[sell_score_column_avg] = df[sell_labels].mean(skipna=True, axis=1)
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if P.buy_sell_equal:
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grid_signals[0]["sell_point_threshold"] = [None]
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grid_signals[0]["sell_window"] = [None]
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grid_signals[0]["sell_signal_threshold"] = [None]
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grid_signals[0]["sell_slope_threshold"] = [None]
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months_in_simulation = (df[time_column].iloc[-1] - df[time_column].iloc[0]) / timedelta(days=30.5)
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performances = list()
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for model in tqdm(ParameterGrid(grid_signals), desc="MODELS"):
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#
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# If equal parameters, then use the first group
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#
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if P.buy_sell_equal:
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model["sell_point_threshold"] = model["buy_point_threshold"]
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model["sell_window"] = model["buy_window"]
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model["sell_signal_threshold"] = model["buy_signal_threshold"]
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model["sell_slope_threshold"] = model["buy_slope_threshold"]
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#
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# Add two pairs of columns: buy_score_column/sell_score_column and buy_signal_column/sell_signal_column
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#
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generate_signal_columns(df, model, buy_score_column_avg, sell_score_column_avg)
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#
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# Simulate trade using close price and two boolean signals
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# Add a pair of two dicts: performance dict and model parameters dict
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#
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performance, long_performance, short_performance = \
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simulated_trade_performance(df, 'sell_signal_column', 'buy_signal_column', 'close')
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# Remove some items. Remove lists of transactions which are not needed
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long_performance.pop('transactions', None)
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short_performance.pop('transactions', None)
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# Add some metrics. Add per month metrics
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performance["profit_percent_per_month"] = performance["profit_percent"] / months_in_simulation
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performance["transaction_no_per_month"] = performance["transaction_no"] / months_in_simulation
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performance["profit_percent_per_transaction"] = performance["profit_percent"] / performance["transaction_no"] if performance["transaction_no"] else 0.0
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performance["profit_per_month"] = performance["profit"] / months_in_simulation
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long_performance["profit_percent_per_month"] = long_performance["profit_percent"] / months_in_simulation
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short_performance["profit_percent_per_month"] = short_performance["profit_percent"] / months_in_simulation
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performances.append(dict(
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model=model,
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performance={k: performance[k] for k in ['profit_percent_per_month', 'profitable', 'profit_percent_per_transaction', 'transaction_no_per_month']},
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long_performance={k: long_performance[k] for k in ['profit_percent_per_month', 'profitable']},
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short_performance={k: short_performance[k] for k in ['profit_percent_per_month', 'profitable']}
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))
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#
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# Flatten
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#
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# Sort
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performances = sorted(performances, key=lambda x: x['performance']['profit_percent_per_month'], reverse=True)
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performances = performances[:P.topn_to_store]
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# Column names (from one record)
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keys = list(performances[0]['model'].keys()) + \
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list(performances[0]['performance'].keys()) + \
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list(performances[0]['long_performance'].keys()) + \
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list(performances[0]['short_performance'].keys())
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lines = []
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for p in performances:
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record = list(p['model'].values()) + \
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list(p['performance'].values()) + \
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list(p['long_performance'].values()) + \
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list(p['short_performance'].values())
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record = [f"{v:.2f}" if isinstance(v, float) else str(v) for v in record]
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record_str = ",".join(record)
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lines.append(record_str)
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#
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# Store simulation parameters and performance
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#
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out_path = (out_path / App.config.get("signal_models_file_name")).with_suffix(".txt").resolve()
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if out_path.is_file():
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add_header = False
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else:
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add_header = True
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with open(out_path, "a+") as f:
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if add_header:
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f.write(",".join(keys) + "\n")
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#f.writelines(lines)
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f.write("\n".join(lines) + "\n\n")
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print(f"Simulation results stored in: {out_path}. Lines: {len(lines)}.")
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elapsed = datetime.now() - now
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print(f"Finished simulation in {str(elapsed).split('.')[0]}")
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if __name__ == '__main__':
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main()
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