def push():
# DBから必要なデータを取得
today = db_session.query(Data).all()[-2].date
nextday = db_session.query(Data).all()[-1].date
price_today = db_session.query(Data).all()[-2].real_price
price_nextday = db_session.query(Data).all()[-1].pred_price
# 送信するメッセージを記述
text = (
"おはようございます！🌅 \n本日のビットコイン相場予測配信です！💹\n\n"
f"{today.strftime('%Y-%m-%d %H:%M')}の実際相場\n"
f"￥{price_today} (BTC/JPY)\n\n"
f"{nextday.strftime('%Y-%m-%d %H:%M')}の予測相場\n"
f"￥{price_nextday} (BTC/JPY)\n\n"
"アプリケーションのURL \n"
"https://predict-bitcoin.herokuapp.com/"
)
messages = TextSendMessage(text=text)
# アカウントを追加している全ユーザーにメッセージを送信
line_bot_api.broadcast(messages=messages)
def predict_nextday():
# 価格情報を取得しデータフレームに格納
today_data = db_session.query(Data).all()[-1]
today_data.real_price = get_price() # 上書き（上書きする前はNoneになっている）
db_session.add(today_data)
db_session.commit()
# windowを作成し標準化
window = 5
data_five = db_session.query(Data).all()[-window:]
input_will = []
for price in data_five:
input_will.append(price.real_price)
input_will = np.array(input_will).reshape(1, window, 1)
input_mean = np.mean(input_will)
input_std = np.std(input_will)
sta_input = (input_will - input_mean) / input_std
# 学習済みモデルを取得
with open("lstm_model.pickle", "rb") as f:
model = pickle.load(f)
# 翌日の価格を予測、標準化してあるので元に戻す
future_price = model.predict(sta_input)
future_price = future_price.reshape(-1)
future_price = future_price * input_std + input_mean
future_price = float(future_price)
# 翌日の日付を取得
today = db_session.query(Data).all()[-1].date
next_day = today + timedelta(days=1)
# 翌日の日付、予測した価格をデータベースに格納
row = Data(date=next_day, real_price=None, pred_price=future_price)
db_session.add(row)
db_session.commit()
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
import pandas as pd
import datetime
from assets.database import db_session
from assets.models import Data
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
data = db_session.query(Data.date, Data.subscribers, Data.reviews).all()
dates = []
subscribers = []
reviews = []
for datum in data:
dates.append(datum.date)
subscribers.append(datum.subscribers)
reviews.append(datum.reviews)
diff_subscribers = pd.Series(subscribers).diff().values
diff_reviews = pd.Series(reviews).diff().values
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server #herokuにデプロイする際に必要
app.layout = html.Div(children=[
html.H2(children='PythonによるWebスクレイピング～アプリケーション偏～'),
from assets.database import init_db from assets.models import Data import datetime init_db() #create date = datetime.date.today() row1 = Data(date=date, subscribers=6500, reviews=210) row2 = Data(date=date, subscribers=1500, reviews=220) db_session.add(row1) db_session.add(row2) db_session.commit() #commitでデータベースに保存 #read read1 = db_session.query(Data).all()[0].subscribers #update datum = db_session.query(Data).all()[0] datum.subscribers = 1000 db_session.add(datum) db_session.commit() #dalete datum = db_session.query(Data).filter_by(subscribers=1000).one() db_session.delete(datum) db_session.commit() #全削除 #Data.query.delete() print(db_session.query(Data).all())
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
from assets.database import db_session
from assets.models import Data
# 外部のスタイルシートを取得
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
# インスタンスを生成
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
server = app.server
# DBからデータを取得
data = db_session.query(Data.date, Data.real_price, Data.pred_price).all()
# 取得したデータを項目ごとに分配
dates = []
real_prices = []
pred_prices = []
for _data in data:
dates.append(_data.date)
real_prices.append(_data.real_price)
pred_prices.append(_data.pred_price)
# トップのコンポーネントを作成
top = html.H2(children='ビットコインの相場予測 （ BTC / JPY ）',
style={
'textAlign': 'center',