prvni draft LSTM
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David Brazda
331
v2realbot/LSTMtrain.py
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331
v2realbot/LSTMtrain.py
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from keras.models import Sequential, load_model
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from keras.layers import LSTM, Dense
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import matplotlib.pyplot as plt
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from v2realbot.controller.services import get_archived_runner_details_byID
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from v2realbot.common.model import RunArchiveDetail
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from v2realbot.config import DATA_DIR
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from v2realbot.utils.utils import slice_dict_lists
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from collections import defaultdict
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from operator import itemgetter
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from joblib import dump, load
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#ZAKLAD PRO TRAINING SCRIPT na vytvareni model
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# TODO
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# podpora pro BINARY TARGET
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# podpora hyperpamaetru (activ.funkce sigmoid atp.)
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# udelat vsechny config vars do cfg objektu
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# dopracovat identifikatory typu lastday close, todays open atp.
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# random SEARCG a grid search
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# udelat nejaka model metadata (napr, trenovano na (runners+obdobi), nastaveni treningovych dat, počet epoch, hyperparametry, config atribu atp.) - mozna persistovat v db
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# udelat nejake verzovani
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# predelat do GUI a modulu
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# prepare data do importovane funkce, aby bylo mozno pouzit v predict casti ve strategii a nemuselo se porad udrzovat
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#s nastavenim modelu. To stejne i s nastavenim upravy features
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#TODO NAPADY Na modely
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#binary identifikace trendu napr. pokud nasledujici 3 bary rostou (0-1)
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#soustredit se na modely s vystupem 0-1 nebo -1 až 1
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# Sample data (replace this with your actual OHLCV data)
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bars = {
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'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15],
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'high': [10, 11, 12, 13, 14,10, 11, 12, 13, 14,10, 11, 12, 13, 14],
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'low': [8, 9, 7, 6, 8,8, 9, 7, 6, 8,8, 9, 7, 6, 8],
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'volume': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300],
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'close': [9, 10, 11, 12, 13,9, 10, 11, 12, 13,9, 10, 11, 12, 13],
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'open': [9, 10, 8, 8, 8,9, 10, 8, 8, 8,9, 10, 8, 8, 8],
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'resolution': [1, 1, 1, 1, 1,1, 1, 1, 1, 1,1, 1, 1, 1, 1]
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}
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indicators = {
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'time': [1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15],
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'fastslope': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115],
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'fsdelta': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115],
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'fastslope2': [90, 95, 100, 110, 115,90, 95, 100, 110, 115,90, 95, 100, 110, 115],
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'ema': [1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300,1000, 1200, 900, 1100, 1300]
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}
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# Zakladni nastaveni
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testlist_id = ""
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runner_ids = ["838e918e-9be0-4251-a968-c13c83f3f173","c11c5cae-05f8-4b0a-aa4d-525ddac81684"]
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features = ["time","high","low","volume","open","close", "trades", "vwap","samebarslope", "fastslope","fsdelta", "fastslope2", "fsdelta2"]
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#TODO toto je linearni prediction mod, dodelat podporu BINARY
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#u binary bude target bud hotovy indikator a nebo jej vytvorit on the fly
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#model muze byt take bez barů, tzn. jen indikatory
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use_bars = True
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target = 'fastslope2'
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#predict how many bars in the future
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target_steps = 5
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name = "model1"
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seq = 10
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epochs = 200
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#crossday identifier je time (hodnota resolution je pouzita ne odstraneni sekvenci skrz dny)
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#predpoklad pouziti je crossday_sequence je time ve features
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resolution = 1
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crossday_sequence = False
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#zda se model uci i crosseday (skrz runner/day data). Pokud ne, pak se crossday sekvence odstrani
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#realizovano pomoci pomocneho identifikatoru (runner)
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#zajistime poradi
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features.sort()
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#cas na prvnim miste
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if "time" in features:
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features.remove("time")
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features.insert(0, "time")
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def merge_dicts(dict_list):
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# Initialize an empty merged dictionary
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merged_dict = {}
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# Iterate through the dictionaries in the list
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for i,d in enumerate(dict_list):
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for key, value in d.items():
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if key in merged_dict:
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merged_dict[key] += value
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else:
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merged_dict[key] = value
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#vlozime element s idenitfikaci runnera
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return merged_dict
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# # Initialize the merged dictionary with the first dictionary in the list
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# merged_dict = dict_list[0].copy()
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# merged_dict["index"] = []
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# # Iterate through the remaining dictionaries and concatenate their lists
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# for i, d in enumerate(dict_list[1:]):
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# merged_dict["index"] =
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# for key, value in d.items():
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# if key in merged_dict:
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# merged_dict[key] += value
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# else:
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# merged_dict[key] = value
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# return merged_dict
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def load_runner(runner_id):
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res, sada = get_archived_runner_details_byID(runner_id)
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if res == 0:
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print("ok")
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else:
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print("error",res,sada)
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bars = sada["bars"]
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indicators = sada["indicators"][0]
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return bars, indicators
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def prepare_data(bars, indicators, features, target) -> tuple[np.array, np.array]:
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#create SOURCE DATA with features
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# bars and indicators dictionary and features as input
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indicator_data = np.column_stack([indicators[feature] for feature in features if feature in indicators])
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if len(bars)>0:
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bar_data = np.column_stack([bars[feature] for feature in features if feature in bars])
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combined_day_data = np.column_stack([bar_data,indicator_data])
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else:
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combined_day_data = indicator_data
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#create TARGET DATA
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try:
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target_base = bars[target]
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except KeyError:
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target_base = indicators[target]
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target_day_data = np.column_stack([target_base])
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return combined_day_data, target_day_data
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def load_runners_as_list(runner_ids: list, use_bars: bool):
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"""Loads all runners data (bars, indicators) for runner_ids into list of dicts-
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Args:
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runner_ids: list of runner_ids.
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use_bars: Whether to use also bars or just indicators
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Returns:
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tuple (barslist, indicatorslist) - lists with dictionaries for each runner
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"""
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barslist = []
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indicatorslist = []
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for runner_id in runner_ids:
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bars, indicators = load_runner(runner_id)
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if use_bars:
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barslist.append(bars)
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indicatorslist.append(indicators)
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return barslist, indicatorslist
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def create_sequences(combined_data, target_data, seq, target_steps, crossday_sequence = True):
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"""Creates sequences of given length seq and target N steps in the future.
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Args:
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combined_data: A list of combined data.
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target_data: A list of target data.
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seq: The sequence length.
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target_steps: The number of steps in the future to target.
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crossday_sequence: Zda vytvaret sekvenci i skrz dny (runnery)
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Returns:
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A list of X sequences and a list of y sequences.
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"""
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X_train = []
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y_train = []
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last_delta = None
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for i in range(len(combined_data) - seq - target_steps):
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if last_delta is None:
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last_delta = 2*(combined_data[i + seq + target_steps, 0] - combined_data[i, 0])
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curr_delta = combined_data[i + seq + target_steps, 0] - combined_data[i, 0]
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#pokud je cas konce sequence vyrazne vetsi (2x) nez predchozi
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#print(f"standardní zacatek {combined_data[i, 0]} konec {combined_data[i + seq + target_steps, 0]} delta: {curr_delta}")
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if crossday_sequence is False and curr_delta > last_delta:
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print(f"sekvence vyrazena. Zacatek {combined_data[i, 0]} konec {combined_data[i + seq + target_steps, 0]}")
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continue
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X_train.append(combined_data[i:i + seq])
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y_train.append(target_data[i + seq + target_steps])
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last_delta = 2*(combined_data[i + seq + target_steps, 0] - combined_data[i, 0])
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return np.array(X_train), np.array(y_train)
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barslist, indicatorslist = load_runners_as_list(runner_ids, use_bars)
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#zmergujeme vsechny data dohromady
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bars = merge_dicts(barslist)
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indicators = merge_dicts(indicatorslist)
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print(f"{len(indicators)}")
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print(f"{len(bars)}")
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source_data, target_data = prepare_data(bars, indicators, features, target)
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# Set the printing threshold to print only the first and last 10 rows of the array
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np.set_printoptions(threshold=10)
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print("source_data", source_data, "shape", np.shape(source_data))
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# Standardize the data
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scalerX = StandardScaler()
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scalerY = StandardScaler()
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#FIT SCALER také fixuje počet FEATURES !!
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source_data = scalerX.fit_transform(source_data)
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target_data = scalerY.fit_transform(target_data)
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#print("source_data shape",np.shape(source_data))
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# Create a sequence of seq elements and define target prediction horizona
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X_train, y_train = create_sequences(source_data, target_data, seq=seq, target_steps=target_steps, crossday_sequence=crossday_sequence)
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#X_train (6205, 10, 14)
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print("X_train", np.shape(X_train))
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X_complete = np.array(X_train.copy())
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Y_complete = np.array(y_train.copy())
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X_train = np.array(X_train)
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y_train = np.array(y_train)
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# Split the data into training and test sets
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X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, shuffle=False) #random_state=42)
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#print(np.shape(X_train))
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# Define the input shape of the LSTM layer dynamically based on the reshaped X_train value
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input_shape = (X_train.shape[1], X_train.shape[2])
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# Build the LSTM model
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model = Sequential()
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model.add(LSTM(128, input_shape=input_shape))
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model.add(Dense(1))
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# Compile the model
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model.compile(loss='mse', optimizer='adam')
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# Train the model
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model.fit(X_train, y_train, epochs=epochs)
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#save the model
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#model.save(DATA_DIR+'/my_model.keras')
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#model = load_model(DATA_DIR+'/my_model.keras')
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dump(scalerX, DATA_DIR+'/'+name+'scalerX.pkl')
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dump(scalerY, DATA_DIR+'/'+name+'scalerY.pkl')
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dump(model, DATA_DIR+'/'+name+'.pkl')
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model = load(DATA_DIR+'/'+ name +'.pkl')
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scalerX: StandardScaler = load(DATA_DIR+'/'+ name +'scalerX.pkl')
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scalerY: StandardScaler = load(DATA_DIR+'/'+ name +'scalerY.pkl')
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#LIVE PREDICTION - IMAGINE THIS HAPPENS LIVE
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# Get the live data
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# Prepare the data for bars and indicators
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#asume ohlc_features and indicator_features remain the same
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#get last 5 items of respective indicators
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#mazeme runner indikator pokud tu je
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if "runner" in indicators:
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del indicators["runner"]
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print("runner key deleted from indicators")
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if "runner" in features:
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features.remove("runner")
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print("runner removed from features")
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lastNbars = slice_dict_lists(bars, seq)
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lastNindicators = slice_dict_lists(indicators, seq)
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print("last5bars", lastNbars)
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print("last5indicators",lastNindicators)
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indicator_data = np.column_stack([lastNindicators[feature] for feature in features if feature in lastNindicators])
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if use_bars:
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bar_data = np.column_stack([lastNbars[feature] for feature in features if feature in lastNbars])
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combined_live_data = np.column_stack([bar_data, indicator_data])
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else:
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combined_live_data = indicator_data
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print("combined_live_data",combined_live_data)
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combined_live_data = scalerX.transform(combined_live_data)
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#scaler = StandardScaler()
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combined_live_data = np.array(combined_live_data)
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#converts to 3D array
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# 1 number of samples in the array.
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# 2 represents the sequence length.
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# 3 represents the number of features in the data.
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combined_live_data = combined_live_data.reshape((1, seq, combined_live_data.shape[1]))
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# Make a prediction
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prediction = model(combined_live_data, training=False)
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#prediction = prediction.reshape((1, 1))
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# Convert the prediction back to the original scale
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prediction = scalerY.inverse_transform(prediction)
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print("prediction for last value", float(prediction))
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#TEST PREDICATIONS
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# Evaluate the model on the test set
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#pozor testovaci sadu na produkc scalovat samostatne
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#X_test = scalerX.transform(X_test)
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#predikce nad testovacimi daty
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X_complete = model.predict(X_complete)
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X_complete = scalerY.inverse_transform(X_complete)
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#target testovacim dat
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Y_complete = scalerY.inverse_transform(Y_complete)
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mse = mean_squared_error(Y_complete, X_complete)
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print('Test MSE:', mse)
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# Plot the predicted vs. actual close prices
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plt.plot(Y_complete, label='Actual')
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plt.plot(X_complete, label='Predicted')
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plt.legend()
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plt.show()
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# To make a prediction, we can simply feed the model a sequence of 5 elements and it will predict the next element. For example, to predict the close price for the 6th time period, we would feed the model the following sequence:
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# sequence = combined_data[0:5]
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# prediction = model.predict(sequence)
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