Recurrent Neural Network RNN Ⅱ

RNN 豪ドル/円 ４時間足予測最新表示ファイルまとめ

RNN 豪ドル/円 ４時間足予測最新表示 PY ファイル keras-go-4h-4.py をまとめます。
keras-go-4h-3.py から変更した部分は赤字にしています。

# -*- coding: utf-8 -*-
#!/usr/bin/python3

import datetime
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tensorflow import keras
from mplfinance.original_flavor import candlestick_ohlc

def addBusinessDays_4h(current_date, business_days_to_add_h):
# current_date : 処理する初日の日時 例) 2022-02-03 00:00:00
# business_days_to_add_h : 4時間ずつ延長する回数 
  cur_date8 = [] # 21/03/30 12～
  cur_date4 = [] # 3/30
  man_f = 1 #月曜フラグ 0 or 1
  doyo_f = 0  #土曜フラグ 0 or 1
  current_date0 = current_date
  weekday0 = current_date0.weekday()
  if(weekday0 == 0): #月
    man_f = 0
    
  while business_days_to_add_h > 0:
    current_date += datetime.timedelta(hours=4)
    weekday = current_date.weekday()
    hour_doyo = current_date.hour
    if weekday == 6: # sunday = 6
      continue
    if((weekday == 5) and (hour_doyo == 8)): #土
      doyo_f = 1
    if((weekday == 5) and (doyo_f == 1)): #土
      continue
        
    if((weekday == 0) and (man_f == 1)): #月
      man_f = 0 
      continue
  # 年/月/日 時刻として文字列にする処理
  # 例 22/03/30 12～     
    e11 = current_date.strftime("%y/%m/%d %H")
    e22 = e11.replace("/0", "/")
    e22 = e22 + "～"
    e33 = e22[3:] # 22/ 削除
    cur_date8.append(e22)
    cur_date4.append(e33)
    business_days_to_add_h -= 1
  return cur_date8, cur_date4

xl_df = pd.read_csv(
# "/home/yamada/public_html/colab/colab_mane_chart_go_4h.csv",
  "/home/yamada/public_html/manep_4h/mane_chart_go_4h.csv",
  encoding="cp932")

raw_data0 = xl_df["終値(売り)"].values
print("raw_data0.shape:", raw_data0.shape)
print("raw_data0")
print(raw_data0)
plt.plot(range(len(raw_data0)), raw_data0)
plt.show()
raw_data = raw_data0.copy()
# 行列の平均、標準偏差を求めます。
mean = np.mean(raw_data)
print("Mean", mean)
raw_data -= mean
std = np.std(raw_data)
print("Std ",std)
# 標準偏差値に変換
raw_data /= std
print("各標準偏差値")
print(raw_data)

# 連続デ－タとする。一つおきは、2
sampling_rate = 1
# 過去20間隔デ－タをひとまとまりとして時系列予測する
sequence_length = 20
delay = sampling_rate * sequence_length
print("delay:", delay)
batch_size = 32  # 適当
# 検証デ－タのスタ－ト値
num_half_samples = int(0.5 * len(raw_data))

train_dataset = keras.utils.timeseries_dataset_from_array(
  raw_data,
  targets=raw_data[delay:],
  sampling_rate=sampling_rate,
  sequence_length=sequence_length,
  batch_size=batch_size,
)
val_dataset = keras.utils.timeseries_dataset_from_array(
  raw_data[:-1],
  targets=raw_data[delay:],
  sampling_rate=sampling_rate,
  sequence_length=sequence_length,
  batch_size=batch_size,
  start_index=num_half_samples,
)
test_dataset = keras.utils.timeseries_dataset_from_array(
  raw_data,
  targets=None,
  sampling_rate=sampling_rate,
  sequence_length=sequence_length,
  batch_size=batch_size,
)
# numpy ndarray 配列に変換して表示
# 訓練デ－タセット表示
itr = 0
for samples, targets in train_dataset:
  samples_n = samples.numpy()
  targets_n = targets.numpy()
  if itr == 0:
    print("Start in-train:", samples_n[0])
    print("Start tar-train:", targets_n[0])
    itr = itr + 1
print("End in-train:", samples_n[-1])
print("End tar-train:", targets_n[-1])
# 検証デ－タセット表示
itv = 0
for samples_v, targets_v in val_dataset:
  samples_vn = samples_v.numpy()
  targets_vn = targets_v.numpy()
  if itv == 0:
    print("Start in-val:", samples_vn[0])
    print("Start tar-val:", targets_vn[0])
    itv = itv + 1
print("End in-val:", samples_vn[-1])
print("End tar-val:", targets_vn[-1])
# テストデ－タセット表示
i = 0
for inputs_t in test_dataset:
  inputs_n = inputs_t.numpy()
  if i == 0:
    print("Start test:", inputs_n[0])
    i = i + 1
print("End test:", inputs_n[-1])

#------ここまで keras-test-51.ipynb 
# from tensorflow import keras

from keras import layers
from keras import initializers

sequence_length = 20
inputs = keras.Input(shape=(sequence_length,))
x = layers.Flatten()(inputs)
x = layers.Dense(
    20,
    activation="tanh",
    kernel_initializer='zeros'
    )(x)
outputs = layers.Dense(1)(x)
model = keras.Model(inputs, outputs)
print("モデルア－キテクチャ")
print(model.summary())

#---ここまで keras-test-52.ipynb
 
callbacks_list = [
  keras.callbacks.EarlyStopping(
    monitor="val_loss",
    patience=2,
    ),
  keras.callbacks.ModelCheckpoint(
#   "drive/MyDrive/Colab Notebooks/my_data/jena_dense_4h.keras",
    "/home/yamada/public_html/colab/jena_dense_4h.keras",
    save_best_only=True,
    )
]
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
history = model.fit(
  train_dataset,
  epochs=40,
  # verbose=0,
  validation_data=val_dataset,
  callbacks=callbacks_list)
  
loss = history.history["mae"] # 平均絶対誤差(MAE)
val_loss = history.history["val_mae"]
epochs = range(1, len(loss) + 1)

plt.plot(epochs, loss, "bo", label="Training MAE")
plt.plot(epochs, val_loss, "b", label="Validation MAE")
plt.title("Training and Validation MAE")
plt.legend()
plt.show()
#---ここまで keras-test-53.ipynb
model = keras.models.load_model(
# "drive/MyDrive/Colab Notebooks/my_data/jena_dense_4h.keras")
  "/home/yamada/public_html/colab/jena_dense_4h.keras")
pre = model.predict(test_dataset)
pre1 = np.reshape(pre, (-1))
print(f"Test 予測値:")
print(pre1)

future_test = inputs_t[-1:]
print("future_test 最初の配列値")
print(future_test)
future_result = []
for i in range(6):
  test_data_f = np.reshape(future_test, (1, 20, 1))
  batch_predict = model.predict(test_data_f)
  future_test = np.delete(future_test, 0)
  future_test = np.append(future_test, batch_predict)
  future_result = np.append(future_result, batch_predict)
print("future_result :")
print(future_result)
# ここまで colab-21 まとめ

len_raw_data = len(raw_data)
xx1 = np.arange(sequence_length, len_raw_data + 1)

xx3 = np.arange(len_raw_data, len_raw_data + 6)

plt.plot(xx1, pre)
plt.plot(xx3, future_result)
plt.show()
#ここまで colab-22①まとめ

pre_chg = pre.copy()
pre_chg *= std
pre_chg += mean

pre_chg1 = np.reshape(pre_chg, (-1))
print("pre_chg1:")
print(pre_chg1)

f_result = future_result.copy()
f_result *= std
f_result += mean
print("f_result:")
print(f_result)
plt.plot(range(len(raw_data0)), raw_data0)
plt.plot(xx1, pre_chg)
plt.plot(xx3, f_result)
plt.show()
#ここまで colab-22②まとめ

# ここから４時間ロ－ソク足表示

Open = xl_df["始値(売り)"].values
High = xl_df["高値(売り)"].values
Low = xl_df["安値(売り)"].values
Close = xl_df["終値(売り)"].values

Date = xl_df["日付"].values
Idx = xl_df.index
# X 軸日付処理
xDate = []
xD = []
dayf = 0

for i, key in enumerate(Date):
  day00 = str(key)[11:19]
  if((dayf == 1) and (day00 == '00:00:00')):
    dayf = 0
  else:
    if((dayf == 0) and (day00 == '00:00:00')):
      e4 = str(key)[4:10]
      e6 = e4.replace("/0", "/")
      e8 = e6.lstrip("/")
      xDate.append(e8)
      xD.append(i)
      dayf = 1
tstr = Date[-1]
if(dayf == 0):
  e4l = tstr[7:14]
  e6l = e4l.replace("/0", "/")
  e8l = e6l.lstrip("/")
  xDate.append(e8l)
  xD.append(i)
# ロ－ソク４時間足表示    
ohlc = zip(
 Idx, Open, High, Low, Close)
fig = plt.figure(
 figsize=(8.34, 5.56))
ax = fig.add_subplot(1,1,1)  #④
ax.grid()
'''ここに予測値を入れる取り敢えず
Idx6 = Idx[-6:] + 6
app = Close[-1:]
app1 = np.hstack((app, app, app, app, app, app))
plt.plot(
 Idx6, app1, 'bo')
として表示する
以下に正しい予測値に修正
'''
plt.plot(xx1, pre_chg)
plt.plot(xx3, f_result, 'bo')

candlestick_ohlc(
 ax, ohlc, width=0.5, alpha = 1,
 colorup='r', colordown='g')
plt.xticks(xD, xDate)
plt.title('AUS$ / JPY chart')
plt.xlabel('Date')
plt.ylabel('Yen')
plt.show
plt.savefig(
# '/home/yamada/public_html/manep-img/colab_mane_chart_go_keras_4h.png')
  '/home/yamada/public_html/manep-img/mane_chart_go_keras_4h.png')
# ここから４時間足予測最終日時取得
tstr = Date[-1]
tdatetime = datetime.datetime.strptime(tstr, '%Y/%m/%d %H:%M:%S')
lastday = addBusinessDays_4h(tdatetime, 6)

# app = Close[-1:]
# f_result = np.hstack((app, app, app, app, app, app))

lastday_pd = pd.DataFrame(lastday[0])
f_result_pd = pd.DataFrame(np.round(f_result, 3))
# 有効数字3桁に変更
df_concat = pd.concat([lastday_pd, f_result_pd], axis = 1)
df_concat.to_csv(
# '/home/yamada/public_html/manep-img/colab_mane_chart_go_keras_4h.csv',
  '/home/yamada/public_html/manep-img/mane_chart_go_keras_4h.csv',
  header=False, index=False)