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dsyoon
2022-08-13 17:16:47 +09:00
parent 8ec05b8447
commit f9ffa363fa
5 changed files with 100 additions and 126 deletions
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12
Simulation.py
View File

@@ -1,4 +1,4 @@
import time import numpy as np
from math import nan from math import nan
import pandas as pd import pandas as pd
import plotly.graph_objects as go import plotly.graph_objects as go
@@ -152,9 +152,9 @@ class Simulation (HTS):
self.labelMaker.showLabels(stock_code, today) self.labelMaker.showLabels(stock_code, today)
else: else:
if method == "ml": if method == "ml":
LAST_DATA = self.stock2Vector.getLastData(stock_code, today, n=1) LAST_DATA = self.stock2Vector.getLastData(stock_code, today, n=3)
data = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA) data = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
X, Y = self.stock2Vector.getDataset2D(data) X, Y = self.stock2Vector.getVectorData(data)
predY = self.stockPredictor.predict(X, Y) predY = self.stockPredictor.predict(X, Y)
bsLine = None bsLine = None
@@ -180,11 +180,11 @@ if __name__ == "__main__":
# to check bying # to check bying
stock_codes = { stock_codes = {
"252670": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811'], "252670": ['20220805', '20220808', '20220809', '20220810', '20220811'],
"122630": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811'], "122630": ['20220805', '20220808', '20220809', '20220810', '20220811'],
} }
method = "rule" # "rule", "ml", "answer" method = "ml" # "rule", "ml", "answer"
for stock_code in stock_codes: for stock_code in stock_codes:
simulation = Simulation(RESOURCE_PATH) simulation = Simulation(RESOURCE_PATH)

126
VitTrainer.py
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@@ -1,11 +1,13 @@
# tensor - numpy - PILImage 변환 (https://qlsenddl-lab.tistory.com/37) # tensor - numpy - PILImage 변환 (https://qlsenddl-lab.tistory.com/37)
import os import os
os.environ['KMP_DUPLICATE_LIB_OK']='True' os.environ['KMP_DUPLICATE_LIB_OK']='True'
import random import random
import numpy as np import numpy as np
import torch import torch
from datasets import Dataset, load_dataset from datasets import Dataset, load_metric, ClassLabel
from datasets import load_metric from datasets import load_metric
from transformers import AutoConfig
from transformers import TrainingArguments, Trainer from transformers import TrainingArguments, Trainer
from transformers import ViTForImageClassification from transformers import ViTForImageClassification
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
@@ -44,14 +46,14 @@ class VitTrainer:
save_strategy="epoch", save_strategy="epoch",
evaluation_strategy="epoch", evaluation_strategy="epoch",
learning_rate=2e-5, learning_rate=2e-5,
per_device_train_batch_size=16, per_device_train_batch_size=32,
per_device_eval_batch_size=16, per_device_eval_batch_size=32,
weight_decay=0.01, weight_decay=0.01,
load_best_model_at_end=True, load_best_model_at_end=True,
metric_for_best_model="accuracy", metric_for_best_model="accuracy",
logging_dir=os.path.join(self.RESOURCE_PATH, 'model', 'logs'), logging_dir=os.path.join(self.RESOURCE_PATH, 'model', 'logs'),
remove_unused_columns=False, remove_unused_columns=False,
num_train_epochs=14, num_train_epochs=4,
) )
return return
@@ -117,7 +119,7 @@ class VitTrainer:
train_ds.set_transform(self.train_transforms) train_ds.set_transform(self.train_transforms)
val_ds.set_transform(self.val_transforms) val_ds.set_transform(self.val_transforms)
train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=4) train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=32)
batch = next(iter(train_dataloader)) batch = next(iter(train_dataloader))
for k,v in batch.items(): for k,v in batch.items():
@@ -157,7 +159,7 @@ class VitTrainer:
train_ds.set_transform(self.train_transforms) train_ds.set_transform(self.train_transforms)
val_ds.set_transform(self.val_transforms) val_ds.set_transform(self.val_transforms)
train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=4) train_dataloader = DataLoader(train_ds, collate_fn=self.collate_fn, batch_size=32)
batch = next(iter(train_dataloader)) batch = next(iter(train_dataloader))
for k,v in batch.items(): for k,v in batch.items():
@@ -211,6 +213,14 @@ class VitTrainer:
train_ds = Dataset.from_dict(train_data) train_ds = Dataset.from_dict(train_data)
val_ds = Dataset.from_dict(val_dsta) val_ds = Dataset.from_dict(val_dsta)
features = train_ds.features.copy()
features["label"] = ClassLabel(num_classes=self.num_labels, names=["none", "sell", "buy"])
def adjust_labels(batch):
batch["label"] = [lbl for lbl in batch["label"]]
return batch
train_ds = train_ds.map(adjust_labels, batched=True, features=features)
val_ds = train_ds.map(adjust_labels, batched=True, features=features)
return train_ds, val_ds return train_ds, val_ds
if __name__ == "__main__": if __name__ == "__main__":
@@ -222,107 +232,5 @@ if __name__ == "__main__":
stock_code = "252670" stock_code = "252670"
vitTrainer = VitTrainer(RESOURCE_PATH) vitTrainer = VitTrainer(RESOURCE_PATH)
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220701", eDate="20220731") train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220809", eDate="20220812")
vitTrainer.train(train_ds, val_ds, model_path) vitTrainer.train(train_ds, val_ds, model_path)
"""
print("ym: 2020-07")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200701", eDate="20200731")
vitTrainer.train(train_ds, val_ds, model_path)
print ("ym: 2020-08")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200725", eDate="20200831")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2020-09")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200825", eDate="20200931")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2020-10")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20200925", eDate="20201031")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2020-11")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201025", eDate="20201131")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2020-12")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201125", eDate="20201231")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-01")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20201225", eDate="20210131")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-02")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210125", eDate="20210231")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-03")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210225", eDate="20210331")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-04")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210325", eDate="20210431")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-05")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210425", eDate="20210531")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-06")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210525", eDate="20210631")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-07")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210625", eDate="20210731")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-08")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210725", eDate="20210831")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-09")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210825", eDate="20210931")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-10")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20210925", eDate="20212031")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-11")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211025", eDate="20211131")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2021-12")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211125", eDate="20211231")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-01")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20211225", eDate="20220131")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-02")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220125", eDate="20220231")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-03")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220225", eDate="20220331")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-04")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220325", eDate="20220431")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-05")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220425", eDate="20220531")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-06")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220525", eDate="20220631")
vitTrainer.finetunning(train_ds, val_ds, model_path)
print("ym: 2022-07")
train_ds, val_ds = vitTrainer.getData(stock_code, sDate="20220625", eDate="20220731")
vitTrainer.finetunning(train_ds, val_ds, model_path)
"""

34
hts/BuySellChecker.py
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@@ -1007,22 +1007,34 @@ class BuySellChecker:
avg3 = [item[0] for item in avg3_list] avg3 = [item[0] for item in avg3_list]
avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist() avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
avg5 = [item[0] for item in avg5_list] avg5 = [item[0] for item in avg5_list]
avg6_list = close_df.rolling(window=6).mean().fillna(close[0]).values.tolist()
avg6 = [item[0] for item in avg6_list]
avg9_list = close_df.rolling(window=9).mean().fillna(close[0]).values.tolist()
avg9 = [item[0] for item in avg9_list]
avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist() avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist()
avg10 = [item[0] for item in avg10_list] avg10 = [item[0] for item in avg10_list]
avg12_list = close_df.rolling(window=12).mean().fillna(close[0]).values.tolist()
avg12 = [item[0] for item in avg12_list]
avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist() avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist()
avg20 = [item[0] for item in avg20_list] avg20 = [item[0] for item in avg20_list]
avg24_list = close_df.rolling(window=24).mean().fillna(close[0]).values.tolist()
avg24 = [item[0] for item in avg24_list]
avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist() avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist()
avg30 = [item[0] for item in avg30_list] avg30 = [item[0] for item in avg30_list]
avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist() avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
avg60 = [item[0] for item in avg60_list] avg60 = [item[0] for item in avg60_list]
abs_avg_1 = [max(avg3[i], avg5[i], avg10[i], avg20[i], avg30[i], avg60[i]) - min(avg3[i], avg5[i], avg10[i], avg20[i], avg30[i], avg60[i]) for i in range(0, len(close))] abs_avg_1 = [max(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i], avg24[i], avg30[i], avg60[i]) - min(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i], avg30[i], avg60[i]) for i in range(0, len(close))]
abs_avg_2 = [max(avg3[i], avg5[i], avg10[i], avg20[i], avg30[i]) - min(avg3[i], avg5[i], avg10[i], avg20[i], avg30[i]) for i in range(0, len(close))] abs_avg_2 = [max(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i], avg24[i], avg30[i]) - min(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i], avg24[i], avg30[i]) for i in range(0, len(close))]
abs_avg_3 = [max(avg3[i], avg5[i], avg10[i], avg20[i]) - min(avg3[i], avg5[i], avg10[i], avg20[i]) for i in range(0, len(close))] abs_avg_3 = [max(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i]) - min(avg3[i], avg5[i], avg6[i], avg9[i], avg10[i], avg12[i], avg20[i]) for i in range(0, len(close))]
abs_avg_4 = [max(avg3[i], avg5[i], avg10[i]) - min(avg3[i], avg5[i], avg10[i]) for i in range(0, len(close))] abs_avg_4 = [max(avg3[i], avg5[i], avg6[i], avg9[i], avg12[i]) - min(avg3[i], avg5[i], avg6[i], avg9[i], avg12[i]) for i in range(0, len(close))]
abs_avg_5 = [max(avg3[i], avg5[i]) - min(avg3[i], avg5[i]) for i in range(0, len(close))] abs_avg_5 = [max(avg3[i], avg5[i], avg6[i], avg9[i]) - min(avg3[i], avg5[i], avg6[i], avg9[i]) for i in range(0, len(close))]
abs_avg_6 = [max(avg3[i], avg5[i], avg6[i]) - min(avg3[i], avg5[i], avg6[i]) for i in range(0, len(close))]
diff_avg3_avg5 = [avg3[i]-avg5[i] for i in range(0, len(close))] diff_avg3_avg5 = [avg3[i]-avg5[i] for i in range(0, len(close))]
diff_avg3_avg6 = [avg3[i] - avg6[i] for i in range(0, len(close))]
diff_avg3_avg9 = [avg3[i] - avg9[i] for i in range(0, len(close))]
diff_avg3_avg10 = [avg3[i] - avg10[i] for i in range(0, len(close))] diff_avg3_avg10 = [avg3[i] - avg10[i] for i in range(0, len(close))]
diff_avg3_avg12 = [avg3[i] - avg12[i] for i in range(0, len(close))]
diff_avg3_avg20 = [avg3[i] - avg20[i] for i in range(0, len(close))] diff_avg3_avg20 = [avg3[i] - avg20[i] for i in range(0, len(close))]
diff_avg3_avg30 = [avg3[i] - avg30[i] for i in range(0, len(close))] diff_avg3_avg30 = [avg3[i] - avg30[i] for i in range(0, len(close))]
diff_avg3_avg60 = [avg3[i] - avg60[i] for i in range(0, len(close))] diff_avg3_avg60 = [avg3[i] - avg60[i] for i in range(0, len(close))]
@@ -1037,7 +1049,10 @@ class BuySellChecker:
diff_avg20_avg60 = [avg20[i] - avg60[i] for i in range(0, len(close))] diff_avg20_avg60 = [avg20[i] - avg60[i] for i in range(0, len(close))]
diff_avg30_avg60 = [avg30[i] - avg60[i] for i in range(0, len(close))] diff_avg30_avg60 = [avg30[i] - avg60[i] for i in range(0, len(close))]
diff_avg3_avg5_sign = self.getSign(avg3, avg5) diff_avg3_avg5_sign = self.getSign(avg3, avg5)
diff_avg3_avg6_sign = self.getSign(avg3, avg6)
diff_avg3_avg9_sign = self.getSign(avg3, avg9)
diff_avg3_avg10_sign = self.getSign(avg3, avg10) diff_avg3_avg10_sign = self.getSign(avg3, avg10)
diff_avg3_avg12_sign = self.getSign(avg3, avg12)
diff_avg3_avg20_sign = self.getSign(avg3, avg20) diff_avg3_avg20_sign = self.getSign(avg3, avg20)
diff_avg3_avg30_sign = self.getSign(avg3, avg30) diff_avg3_avg30_sign = self.getSign(avg3, avg30)
diff_avg3_avg60_sign = self.getSign(avg3, avg60) diff_avg3_avg60_sign = self.getSign(avg3, avg60)
@@ -1072,7 +1087,7 @@ class BuySellChecker:
STOCK = [] STOCK = []
for i in range(len(open)): for i in range(len(open)):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i], STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]}) 'avg3': avg3[i], 'avg5': avg5[i],'avg6': avg6[i],'avg9': avg9[i],'avg10': avg10[i],'avg12': avg12[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]})
# stochastic # stochastic
stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5) stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
@@ -1179,16 +1194,19 @@ class BuySellChecker:
temp = { temp = {
"date": point_temp, "date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "open": open, "high": high, "low": low, "close": close, "volume": vol,
"avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg60": avg60, "avg3": avg3, "avg5": avg5, "avg6": avg6, "avg9": avg9, "avg10": avg10, "avg12": avg12, "avg20": avg20, "avg30": avg30, "avg60": avg60,
"upper": upper, "lower": lower, "upper": upper, "lower": lower,
"macd": macd, "macds": macds, "macdo": macdo, "macd": macd, "macds": macds, "macdo": macdo,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
"rsi": rsi, "rsis": rsis, "rsi": rsi, "rsis": rsis,
"changeLine": changeLine, "baseLine": baseLine, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2, "changeLine": changeLine, "baseLine": baseLine, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2,
"diff_price": diff_price, "height": height, "top_tail_height": top_tail_height, "bottom_tail_height": bottom_tail_height, "diff_price": diff_price, "height": height, "top_tail_height": top_tail_height, "bottom_tail_height": bottom_tail_height,
"abs_avg_1": abs_avg_1, "abs_avg_2": abs_avg_2, "abs_avg_3": abs_avg_3, "abs_avg_4": abs_avg_4, "abs_avg_5": abs_avg_5, "abs_avg_1": abs_avg_1, "abs_avg_2": abs_avg_2, "abs_avg_3": abs_avg_3, "abs_avg_4": abs_avg_4, "abs_avg_5": abs_avg_5, "abs_avg_6": abs_avg_6,
"diff_avg3_avg5": diff_avg3_avg5, "diff_avg3_avg5": diff_avg3_avg5,
"diff_avg3_avg6": diff_avg3_avg6,
"diff_avg3_avg9": diff_avg3_avg9,
"diff_avg3_avg10": diff_avg3_avg10, "diff_avg3_avg10": diff_avg3_avg10,
"diff_avg3_avg12": diff_avg3_avg12,
"diff_avg3_avg20": diff_avg3_avg20, "diff_avg3_avg20": diff_avg3_avg20,
"diff_avg3_avg30": diff_avg3_avg30, "diff_avg3_avg30": diff_avg3_avg30,
"diff_avg3_avg60": diff_avg3_avg60, "diff_avg3_avg60": diff_avg3_avg60,

52
stock/util/Stock2Vector.py
View File

@@ -285,9 +285,13 @@ class Stock2Vector(HTS):
Y = np.asarray(Y, dtype='int64') Y = np.asarray(Y, dtype='int64')
return X, Y return X, Y
def getVectorData(self, data, type="avg10", VECTOR_SIZE = 32): def getVectorData(self, data, VECTOR_SIZE = 32):
return self.getVectorData_2(data, VECTOR_SIZE)
def getVectorData_1(self, data, VECTOR_SIZE):
df = self.buySellChecker.getVectorFeature(data) df = self.buySellChecker.getVectorFeature(data)
# avg10, 볼린져밴드 상단과 하단의 차이, rsi, avg3만 이용한다.
# channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3 # channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
avg10 = df['avg10'].tolist() avg10 = df['avg10'].tolist()
diff_upper_lower = df['diff_upper_lower'].tolist() diff_upper_lower = df['diff_upper_lower'].tolist()
@@ -313,11 +317,55 @@ class Stock2Vector(HTS):
h = 0 h = 0
batch_X.append(X) batch_X.append(X)
batch_Y.append(label[i]) batch_Y.append(label[i])
"""
if label[i] == 2:
batch_Y.append([0, 0, 1])
elif label[i] == 1:
batch_Y.append([0, 1, 0])
else:
batch_Y.append([1, 0, 0])
"""
batch_X = np.asarray(batch_X) batch_X = np.asarray(batch_X)
batch_Y = np.asarray(batch_Y, dtype='int64') batch_Y = np.asarray(batch_Y, dtype='int64')
return batch_X, batch_Y return batch_X, batch_Y
def getVectorData_2(self, data, VECTOR_SIZE = 32):
df = self.buySellChecker.getVectorFeature(data)
# avg10, 볼린져밴드 상단과 하단의 차이, rsi, avg3만 이용한다.
# channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
avg3 = df['avg3'].tolist()
avg6 = df['avg6'].tolist()
avg9 = df['avg9'].tolist()
diff_upper_lower = df['diff_upper_lower'].tolist()
rsi = df['rsi'].tolist()
abs_avg_3 = df['abs_avg_3'].tolist()
size = len(avg10)
batch_X, batch_Y = [], []
X = np.zeros((4, VECTOR_SIZE, VECTOR_SIZE))
label = df['label'].tolist()
for i in range(VECTOR_SIZE * VECTOR_SIZE - 1, size):
w, h = 0, 0
for j in range(i - VECTOR_SIZE * VECTOR_SIZE + 1, i + 1):
X[0, h, w] = avg10[j]
X[1, h, w] = diff_upper_lower[j]
X[2, h, w] = abs_avg_3[j]
X[3, h, w] = rsi[j]
w += 1
if w >= VECTOR_SIZE:
w = 0
h += 1
if h >= VECTOR_SIZE:
h = 0
batch_X.append(X)
batch_Y.append(label[i])
batch_X = np.asarray(batch_X)
batch_Y = np.asarray(batch_Y, dtype='int64')
return batch_X, batch_Y
def getDataset3D(self, data, VECTOR_SIZE = 299): def getDataset3D(self, data, VECTOR_SIZE = 299):
df, minmax_df = self.preprocessData(data) df, minmax_df = self.preprocessData(data)

2
stock/util/StockPredictor.py
View File

@@ -102,7 +102,7 @@ class StockPredictor:
def predict(self, X, Y=None): def predict(self, X, Y=None):
print("Data count: ", len(X)) print("Data count: ", len(X))
X = [self.trans(torch.tensor([x])) for x in X] X = [self.trans(torch.tensor(x)) for x in X]
test_X = X test_X = X
test_Y = Y test_Y = Y