質問編集履歴

3

タイトルの変更

2021/08/01 04:48

投稿

6606akira
6606akira

score4

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- Pythonで日本株のスクリーニング
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+ Pythonで日本株のスクリーニング 株価
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誤りの修正

2021/08/01 04:48

投稿

6606akira
6606akira

score4

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  # Variables
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+ tickers = si.tickers_sp500()
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+
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+ tickers = [item.replace(".", "-") for item in tickers] # Yahoo Finance uses dashes instead of dots
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+
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+ index_name = '^GSPC' # S&P 500
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+
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  start_date = datetime.datetime.now() - datetime.timedelta(days=365)
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  end_date = datetime.date.today()
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+ exportList = pd.DataFrame(columns=['Stock', "RS_Rating", "50 Day MA", "150 Day Ma", "200 Day MA", "52 Week Low", "52 week High"])
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+
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+ returns_multiples = []
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+
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  # Index Returns
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- index_df = pdr.get_data_yahoo('^GSPC', start_date, end_date)
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+ index_df = pdr.get_data_yahoo(index_name, start_date, end_date)
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+
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+ index_df['Percent Change'] = index_df['Adj Close'].pct_change()
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+
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+ index_return = (index_df['Percent Change'] + 1).cumprod()[-1]
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  ```
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- これでS&P500の全銘柄の1年分の株価データは入手できるのでが、^GSPC部分を^N225に変更しても日本株の株価データが入手きません。すべての日経採用銘柄の株価データを入手したいとき、どのようなコードを書けばよいのでしょうか?いろいろなサイトで調べたのですが、日本株の場合一括で株価データを入手する手段が見当たりませんでした。
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+ si.tickers_sp500()でS&P500の全銘柄の株価データは入手でき。こような方法ですべての日経採用銘柄の株価データを入手したいとき、どのようなコードを書けばよいのでしょうか?いろいろなサイトで調べたのですが、日本株の場合一括で株価データを入手する手段が見当たりませんでした。
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  ちなみに以下のトレンド解析プログラムで米国株をスクリーニングしています。
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1

プログラム全貌の追加

2021/08/01 04:22

投稿

6606akira
6606akira

score4

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  ```
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  これでS&P500の全銘柄の1年分の株価データは入手できるのですが、^GSPCの部分を^N225に変更しても日本株の株価データが入手できません。すべての日経採用銘柄の株価データを入手したいとき、どのようなコードを書けばよいのでしょうか?いろいろなサイトで調べたのですが、日本株の場合一括で株価データを入手する手段が見当たりませんでした。
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+
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+ ちなみに以下のトレンド解析プログラムで米国株をスクリーニングしています。
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+
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+ ```ここに言語を入力
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+
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+ # Imports
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+
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+ from pandas_datareader import data as pdr
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+
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+ from yahoo_fin import stock_info as si
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+
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+ from pandas import ExcelWriter
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+
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+ import yfinance as yf
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+
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+ import pandas as pd
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+
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+ import datetime
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+
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+ import time
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+
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+ yf.pdr_override()
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+
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+
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+
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+ # Variables
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+
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+ tickers = si.tickers_sp500()
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+
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+ tickers = [item.replace(".", "-") for item in tickers] # Yahoo Finance uses dashes instead of dots
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+
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+ index_name = '^GSPC' # S&P 500
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+
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+ start_date = datetime.datetime.now() - datetime.timedelta(days=365)
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+
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+ end_date = datetime.date.today()
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+
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+ exportList = pd.DataFrame(columns=['Stock', "RS_Rating", "50 Day MA", "150 Day Ma", "200 Day MA", "52 Week Low", "52 week High"])
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+
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+ returns_multiples = []
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+
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+
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+
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+ # Index Returns
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+
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+ index_df = pdr.get_data_yahoo(index_name, start_date, end_date)
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+ index_df['Percent Change'] = index_df['Adj Close'].pct_change()
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+
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+ index_return = (index_df['Percent Change'] + 1).cumprod()[-1]
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+
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+
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+
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+ # Find top 30% performing stocks (relative to the S&P 500)
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+ for ticker in tickers:
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+
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+ # Download historical data as CSV for each stock (makes the process faster)
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+ df = pdr.get_data_yahoo(ticker, start_date, end_date)
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+ df.to_csv(f'{ticker}.csv')
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+
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+
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+ # Calculating returns relative to the market (returns multiple)
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+ df['Percent Change'] = df['Adj Close'].pct_change()
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+ stock_return = (df['Percent Change'] + 1).cumprod()[-1]
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+
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+
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+
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+ returns_multiple = round((stock_return / index_return), 2)
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+ returns_multiples.extend([returns_multiple])
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+
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+
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+
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+ print (f'Ticker: {ticker}; Returns Multiple against S&P 500: {returns_multiple}\n')
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+
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+
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+ # Creating dataframe of only top 30%
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+ rs_df = pd.DataFrame(list(zip(tickers, returns_multiples)), columns=['Ticker', 'Returns_multiple'])
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+ rs_df['RS_Rating'] = rs_df.Returns_multiple.rank(pct=True) * 100
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+ rs_df = rs_df[rs_df.RS_Rating >= rs_df.RS_Rating.quantile(.70)]
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+
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+
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+
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+ # Checking Minervini conditions of top 30% of stocks in given list
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+ rs_stocks = rs_df['Ticker']
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+ for stock in rs_stocks:
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+
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+ try:
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+ df = pd.read_csv(f'{stock}.csv', index_col=0)
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+
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+ sma = [50, 150, 200]
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+
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+ for x in sma:
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+ df["SMA_"+str(x)] = round(df['Adj Close'].rolling(window=x).mean(), 2)
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+
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+
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+
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+ # Storing required values
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+
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+ currentClose = df["Adj Close"][-1]
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+ moving_average_50 = df["SMA_50"][-1]
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+ moving_average_150 = df["SMA_150"][-1]
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+ moving_average_200 = df["SMA_200"][-1]
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+
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+ low_of_52week = round(min(df["Low"][-260:]), 2)
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+ high_of_52week = round(max(df["High"][-260:]), 2)
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+ RS_Rating = round(rs_df[rs_df['Ticker']==stock].RS_Rating.tolist()[0])
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+
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+
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+
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+ try:
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+
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+ moving_average_200_20 = df["SMA_200"][-20]
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+
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+ except Exception:
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+ moving_average_200_20 = 0
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+
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+
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+
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+ # Condition 1: Current Price > 150 SMA and > 200 SMA
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+ condition_1 = currentClose > moving_average_150 > moving_average_200
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+
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+
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+
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+ # Condition 2: 150 SMA and > 200 SMA
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+ condition_2 = moving_average_150 > moving_average_200
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+
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+
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+
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+ # Condition 3: 200 SMA trending up for at least 1 month
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+ condition_3 = moving_average_200 > moving_average_200_20
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+
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+
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+
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+ # Condition 4: 50 SMA> 150 SMA and 50 SMA> 200 SMA
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+ condition_4 = moving_average_50 > moving_average_150 > moving_average_200
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+
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+
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+
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+ # Condition 5: Current Price > 50 SMA
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+ condition_5 = currentClose > moving_average_50
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+
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+
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+
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+ # Condition 6: Current Price is at least 30% above 52 week low
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+ condition_6 = currentClose >= (1.3*low_of_52week)
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+
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+
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+
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+ # Condition 7: Current Price is within 25% of 52 week high
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+ condition_7 = currentClose >= (.75*high_of_52week)
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+
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+ # If all conditions above are true, add stock to exportList
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+ if(condition_1 and condition_2 and condition_3 and condition_4 and condition_5 and condition_6 and condition_7):
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+ exportList = exportList.append({'Stock': stock, "RS_Rating": RS_Rating ,"50 Day MA": moving_average_50, "150 Day Ma": moving_average_150, "200 Day MA": moving_average_200, "52 Week Low": low_of_52week, "52 week High": high_of_52week}, ignore_index=True)
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+ print (stock + " made the Minervini requirements")
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+ except Exception as e:
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+ print (e)
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+ print(f"Could not gather data on {stock}")
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+
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+
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+ exportList = exportList.sort_values(by='RS_Rating', ascending=False)
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+ print('\n', exportList)
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+ writer = ExcelWriter("ScreenOutput.xlsx")
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+ exportList.to_excel(writer, "Sheet1")
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+ writer.save()
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+ ```