Pandasを使ってCSVファイルを読み込んでいますが、以下のエラーが出ます。

NameError: name 'df' is not defined

以下が該当コードです。

data = pd.read_csv('data.csv')
df.plot()

result = "Hello" + 123  # エラー発生

import pandas as pd
import numpy as np
import yfinance as yf
import mplfinance as mpf

# 期間の設定
period = 14

# 株価データを取得（例：日経平均株価のデータを使用）
ticker = yf.Ticker('^N225')
data = ticker.history(start='2024-01-01', end='2024-12-31')

# TRの計算
high_low = data['High'] - data['Low']
high_close = np.abs(data['High'] - data['Close'].shift(1))
low_close = np.abs(data['Low'] - data['Close'].shift(1))

# TRは3つの値の中で最大のもの
tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)

# ATRの計算
atr = tr.rolling(window=period).mean()

# ATRを追加してプロット
apds = [
    mpf.make_addplot(atr, panel=1, color='red', ylabel='ATR')
]

mpf.plot(
    data,
    type='candle',  # ローソク足チャート
    addplot=apds,
    style='yahoo',
    title='Stock Price with ATR',
    ylabel='Price',
    volume=False
)

# 期間の設定
period = 14

# TRの計算
high_low = data['High'] - data['Low']
high_close = np.abs(data['High'] - data['Close'].shift(1))
low_close = np.abs(data['Low'] - data['Close'].shift(1))

# TRは3つの値の中で最大のもの
tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)

# ATRの計算
atr = tr.rolling(window=period).mean()

Failed to get ticker '^N225' reason: Expecting value: line 1 column 1 (char 0)
[*********************100%***********************]  1 of 1 completed

1 Failed download:
['^N225']: YFTzMissingError('$%ticker%: possibly delisted; no timezone found')

pip install -U yfinance

ValueError: Data for column "Open" must be ALL float or int.

ticker = yf.Ticker('^N225')
data = ticker.history(start='2024-01-01', end='2024-12-31')

pip install numpy pandas yfinance mplfinance

import numpy as np
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt

# 期間の設定　'3mo','6mo'から選択
period = '6mo'


# 株価を取得する関数
def get_data():
    df = pd.DataFrame(columns=['code'])
    csv_df = pd.read_csv('screener_result.csv', header=None, skiprows=1, usecols=[0, 1])
    df['code'] = csv_df[0]
    ticker_symbol_dr = []

    for i in range(len(df)):
        ticker_symbol_dr.append((str(df.iloc[i, 0])) + ".T")

    symbol_data = yf.download(ticker_symbol_dr, period=period, threads=False)
    df = pd.DataFrame(symbol_data['Close'])
    df = df.dropna(axis=1)

    return df


data = get_data()
term = len(data)

# 株価データの正規化
normalization_df = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))

# X軸の正規化
x = list(map(lambda v: v / float(term - 1), range(0, term)))

# 4次多項式でカップウィズハンドルの形状を再現
cup_with_handle = list(map(lambda v: (-39.50813 * (v / float(term - 1)) ** 4) + (86.90238 * (v / float(term - 1)) ** 3) -
                                     (59.7321 * (v / float(term - 1)) ** 2) + (13.13785 * (v / float(term - 1))), range(0, term)))

# 4次多項式を描写
plt.plot(x, cup_with_handle)
plt.show()

# 相関関数を計算してカップウィズハンドルのパターンを検索
for i in range(len(normalization_df.columns)):
    a = normalization_df.iloc[:, i].tolist()
    corr = np.corrcoef(a, cup_with_handle)[1, 0]

    if corr > 0.5:
        print(f"{normalization_df.columns[i]}　相関係数：{corr}")

# 期間の設定　'3mo','6mo'から選択
period = '6mo'


# 株価を取得する関数
def get_data():
    df = pd.DataFrame(columns=['code'])
    csv_df = pd.read_csv('screener_result.csv', header=None, skiprows=1, usecols=[0, 1])
    df['code'] = csv_df[0]
    ticker_symbol_dr = []

    for i in range(len(df)):
        ticker_symbol_dr.append((str(df.iloc[i, 0])) + ".T")

    symbol_data = yf.download(ticker_symbol_dr, period=period)
    df = pd.DataFrame(symbol_data['Close'])
    df = df.dropna(axis=1)

    return df

# 株価データの正規化
normalization_df = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0))

# X軸の正規化
x = list(map(lambda v: v / float(term - 1), range(0, term)))

# 4次多項式でカップウィズハンドルの形状を再現
cup_with_handle = list(map(lambda v: (-39.50813 * (v / float(term - 1)) ** 4) + (86.90238 * (v / float(term - 1)) ** 3) -
                                     (59.7321 * (v / float(term - 1)) ** 2) + (13.13785 * (v / float(term - 1))), range(0, term)))

# 相関関数を計算してカップウィズハンドルのパターンを検索
for i in range(len(normalization_df.columns)):
    a = normalization_df.iloc[:, i].tolist()
    corr = np.corrcoef(a, cup_with_handle)[1, 0]

    if corr > 0.5:
        print(f"{normalization_df.columns[i]}　相関係数：{corr}")
        

import pandas as pd
import yfinance as yf
import mplfinance as mpf

# 期間の設定
period = 20

# 株価データを取得（例：日経平均株価のデータを使用）
ticker = '^N225'
data = yf.download(ticker, start='2024-01-01', end='2024-12-31')

# ボリンジャーバンドの計算
sma = data['Close'].rolling(period).mean()  # 移動平均
std = data['Close'].rolling(period).std()  # 標準偏差
upper_band = sma + (std * 2)  # 上部バンド
lower_band = sma - (std * 2)  # 下部バンド

# ボリンジャーバンドを追加してプロット
apds = [
    mpf.make_addplot(sma, color='blue'),
    mpf.make_addplot(upper_band, color='orange'),
    mpf.make_addplot(lower_band, color='orange')
]

mpf.plot(
    data,
    type='candle',  # ローソク足チャート
    addplot=apds,
    style='yahoo',
    title='Stock Price with Bollinger Bands',
    ylabel='Price',
    volume=False
)

# 期間の設定
period = 20

sma = data['Close'].rolling(period).mean()  # 移動平均
std = data['Close'].rolling(period).std()  # 標準偏差
upper_band = sma + (std * 2)  # 上部バンド
lower_band = sma - (std * 2)  # 下部バンド

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


# 中間方程式の計算
def intermediate_equation(x, p, k):

    return p * x ** (k + 1) + (1 - p) - x


# 中間方程式の解を求める
def solve_equation(win_rate, risk_reward_ratio):
    x = 0
    
    while 0 < intermediate_equation(x, win_rate, risk_reward_ratio) <= 1:
        x += 0.001

    if x >= 1:
        x = 1

    return x


# バルサラの破産確率の計算
def calc_risk_of_ruin(win_rate, risk_reward_ratio, risk_per_trade):
    x = solve_equation(win_rate, risk_reward_ratio)

    return x ** (1 / risk_per_trade)


# 勝率とリスクリワード比率のリストを設定
win_rates = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]  # 勝率のリスト
risk_reward_ratios = [0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5]  # リスクリワード比率のリスト
risk_per_trade = 0.05  # 1トレードあたりのリスク割合（5%）

# ヒートマップ用のデータを保存するためのリスト
heatmap_data = []

# 各パラメータの組み合わせに対して破産確率を計算
for risk_reward_ratio in risk_reward_ratios:
    row = []
    for win_rate in win_rates:
        risk_of_ruin = calc_risk_of_ruin(win_rate, risk_reward_ratio, risk_per_trade)
        row.append(risk_of_ruin * 100)  # パーセンテージに変換
    heatmap_data.append(row)

# pandasのデータフレームに変換
df_heatmap = pd.DataFrame(heatmap_data, index=[f'{rr}' for rr in risk_reward_ratios],
                          columns=[f'{wr * 100:.0f}%' for wr in win_rates])

# ヒートマップを作成
plt.figure(figsize=(10, 6))
sns.heatmap(df_heatmap, annot=True, fmt=".2f", cmap="YlOrRd", cbar_kws={'label': 'Risk of Ruin (%)'})
plt.title("Risk of Ruin Heatmap")
plt.xlabel("Win Rate")
plt.ylabel("Risk-Reward Ratio")
plt.show()

# 中間方程式の計算
def intermediate_equation(x, p, k):

    return p * x ** (k + 1) + (1 - p) - x


# 中間方程式の解を求める
def solve_equation(win_rate, risk_reward_ratio):
    x = 0
    
    while 0 < intermediate_equation(x, win_rate, risk_reward_ratio) <= 1:
        x += 0.001

    if x >= 1:
        x = 1

    return x


# バルサラの破産確率の計算
def calc_risk_of_ruin(win_rate, risk_reward_ratio, risk_per_trade):
    x = solve_equation(win_rate, risk_reward_ratio)

    return x ** (1 / risk_per_trade)

pip install yfinance

import pandas as pd
import yfinance as yf

# 企業の証券コードを設定（末尾に.Tを付ける）
ticker = '7203.T'

# 財務データを取得
ticker_info = yf.Ticker(ticker)
cash_flow = ticker_info.cash_flow

# 各データを抽出
cfo = cash_flow.loc['Operating Cash Flow'].fillna(0)
cfi = cash_flow.loc['Investing Cash Flow'].fillna(0)
cff = cash_flow.loc['Financing Cash Flow'].fillna(0)

# データフレームにまとめる
df = pd.DataFrame({
    '営業CF': cfo,
    '投資CF': cfi,
    '財務CF': cff
}).T

df.columns = [col.year for col in df.columns]


# キャッシュフローの分類
def classify_cashflow(cfo, cfi, cff):

    if cfo > 0 and cfi < 0 and cff < 0:
        return "健全型"

    elif cfo > 0 and cfi > 0 and cff < 0:
        return "改善型"

    elif cfo > 0 and cfi < 0 and cff > 0:
        return "積極型"

    elif cfo > 0 and cfi > 0 and cff > 0:
        return "安定型"

    elif cfo < 0 and cfi > 0 and cff > 0:
        return "救済型"

    elif cfo < 0 and cfi > 0 and cff < 0:
        return "リストラ型"

    elif cfo < 0 and cfi < 0 and cff > 0:
        return "勝負型"

    elif cfo < 0 and cfi < 0 and cff < 0:
        return "大幅見直し型"

    else:
        return "未分類"


# 各年の分類結果
for year in df.columns:

    cfo, cfi, cff = df[year]
    category = classify_cashflow(cfo, cfi, cff)
    print(f"{year}年: {category}")

# 財務データを取得
ticker_info = yf.Ticker(ticker)
cash_flow = ticker_info.cash_flow

# 各データを抽出
cfo = cash_flow.loc['Operating Cash Flow'].fillna(0)
cfi = cash_flow.loc['Investing Cash Flow'].fillna(0)
cff = cash_flow.loc['Financing Cash Flow'].fillna(0)

# キャッシュフローの分類
def classify_cashflow(cfo, cfi, cff):

    if cfo > 0 and cfi < 0 and cff < 0:
        return "健全型"

    elif cfo > 0 and cfi > 0 and cff < 0:
        return "改善型"

    elif cfo > 0 and cfi < 0 and cff > 0:
        return "積極型"

    elif cfo > 0 and cfi > 0 and cff > 0:
        return "安定型"

    elif cfo < 0 and cfi > 0 and cff > 0:
        return "救済型"

    elif cfo < 0 and cfi > 0 and cff < 0:
        return "リストラ型"

    elif cfo < 0 and cfi < 0 and cff > 0:
        return "勝負型"

    elif cfo < 0 and cfi < 0 and cff < 0:
        return "大幅見直し型"

    else:
        return "未分類"

import time
from datetime import datetime
from datetime import timedelta
import requests
import hmac
import hashlib
import json
import pandas as pd


apiKey = 'YOUR_API_KEY'
secretKey = 'YOUR_SECRET'

# 設定項目
symbol = 'BTC_JPY'  # 使用する通貨ペア
interval = '1hour'  # 使用する時間軸
order_size = '0.01'  # 注文数量
MA_short_period = 5  # 移動平均線の短期の日数
MA_long_period = 25  # 移動平均線の長期の日数
get_price_period = 10  # 四本値を取得する日数

flag = {
    'order': {
        'side': '',
    },
    'position': {
        'exist': False,
        'side': None,
        'lot': 0.0,
    },
}


# 成行注文を出す関数
def market_order():
    global flag

    while True:
        try:
            timestamp = '{0}000'.format(int(time.mktime(datetime.now().timetuple())))
            method = 'POST'
            endPoint = 'https://api.coin.z.com/private'
            path = '/v1/order'
            reqBody = {
                'symbol': symbol,
                'side': flag['order']['side'],
                'size': order_size,
                'executionType': 'MARKET'
            }

            text = timestamp + method + path + json.dumps(reqBody)
            sign = hmac.new(bytes(secretKey.encode('ascii')), bytes(text.encode('ascii')), hashlib.sha256).hexdigest()

            headers = {
                'API-KEY': apiKey,
                'API-TIMESTAMP': timestamp,
                'API-SIGN': sign
            }

            res = requests.post(endPoint + path, headers=headers, data=json.dumps(reqBody))
            print(json.dumps(res.json(), indent=2))

            flag['order']['side'] = ''

            return

        except Exception as e:
            print(f'action=market_order error={e}')
            print('10秒後にリトライします')
            time.sleep(10)


# 決済注文を出す関数
def close_order():
    global flag

    while True:
        try:
            timestamp = '{0}000'.format(int(time.mktime(datetime.now().timetuple())))
            method = 'POST'
            endPoint = 'https://api.coin.z.com/private'
            path = '/v1/closeBulkOrder'
            reqBody = {
                'symbol': symbol,
                'side': flag['order']['side'],
                'size': order_size,
                'executionType': 'MARKET'
            }

            text = timestamp + method + path + json.dumps(reqBody)
            sign = hmac.new(bytes(secretKey.encode('ascii')), bytes(text.encode('ascii')), hashlib.sha256).hexdigest()

            headers = {
                'API-KEY': apiKey,
                'API-TIMESTAMP': timestamp,
                'API-SIGN': sign
            }

            res = requests.post(endPoint + path, headers=headers, data=json.dumps(reqBody))
            print(json.dumps(res.json(), indent=2))

            return

        except Exception as e:
            print(f'action=market_order error={e}')
            print('10秒後にリトライします')
            time.sleep(10)


# ポジションを確認する関数
def check_positions():
    global flag

    timestamp = '{0}000'.format(int(time.mktime(datetime.now().timetuple())))
    method = 'GET'
    endPoint = 'https://api.coin.z.com/private'
    path = '/v1/positionSummary'

    text = timestamp + method + path
    sign = hmac.new(bytes(secretKey.encode('ascii')), bytes(text.encode('ascii')), hashlib.sha256).hexdigest()
    parameters = {
        'symbol': symbol
    }

    headers = {
        'API-KEY': apiKey,
        'API-TIMESTAMP': timestamp,
        'API-SIGN': sign
    }

    res = requests.get(endPoint + path, headers=headers, params=parameters)
    positions = eval(json.dumps(res.json(), indent=2))
    if not positions['data']['list']:
        flag['position']['lot'] = 0
        flag['position']['side'] = None
        flag['position']['exist'] = False

        print('現在ポジションは存在しません')

        return

    flag['position']['lot'] = float(positions['data']['list'][0]['sumPositionQuantity'])
    flag['position']['side'] = positions['data']['list'][0]['side']
    flag['position']['exist'] = True

    print('ポジション数量：' + str(flag['position']['lot']))
    print('売買区分：' + str(flag['position']['side']))

    return


# 四本値を取得する関数
def get_price_data():
    while True:
        try:
            price_data = []
            response_data = []

            if 0 <= datetime.today().hour <= 5:
                today = datetime.today().date()

            else:
                today = datetime.today().date() + timedelta(days=1)

            for i in range(get_price_period):
                period = i - get_price_period
                date = (today + timedelta(days=period)).strftime('%Y%m%d')
                endPoint = 'https://api.coin.z.com/public'
                path = '/v1/klines?symbol=' + symbol + '&interval=' + interval + '&date=' + date

                response = requests.get(endPoint + path)
                response_data.append(response.json())

            for j in range(len(response_data)):
                for i in response_data[j]['data']:
                    price_data.append({'openTime': i['openTime'],
                                       'openTime_dt': datetime.fromtimestamp(int(i['openTime'][:-3]))
                                      .strftime('%Y/%m/%d %H:%M'),
                                       'open': i['open'],
                                       'high': i['high'],
                                       'low': i['low'],
                                       'close': i['close']})

            print('TIME： ' + str(price_data[-1]['openTime_dt']) + ' H： ' + str(price_data[-1]['high']) + ' L： ' +
                  str(price_data[-1]['low']) + ' C： ' + str(price_data[-1]['close']))

            df_price_data = pd.DataFrame(price_data)

            return df_price_data

        except Exception as e:
            print(f'action=get_price_data error={e}')
            print('10秒後にリトライします')
            time.sleep(10)

# 移動平均線のGC、DCを判定してエントリー注文を出す関数
def entry_signal(df):
    global flag

    df['MA-short'] = df['open'].rolling(MA_short_period).mean()
    df['MA-long'] = df['open'].rolling(MA_long_period).mean()

    if df.iat[-2, 6] < df.iat[-5, 7] and df.iat[-1, 6] > df.iat[-4, 7]:
        flag['order']['side'] = 'BUY'
        print('ゴールデンクロスが発生しました。')

    elif df.iat[-2, 6] > df.iat[-2, 7] and df.iat[-1, 6] < df.iat[-1, 7]:
        flag['order']['side'] = 'SELL'
        print('デッドクロスが発生しました。')

    else:
        flag['order']['side'] = ''

    return flag


# メイン処理
check_positions()

while True:

    price = get_price_data()
    entry_signal(price)

    if flag['position']['exist'] is False:
        if flag['order']['side'] == 'BUY':
            print('買いの成行注文を出します。')
            market_order()
            time.sleep(10)

            check_positions()
            time.sleep(60)

        if flag['order']['side'] == 'SELL':
            print('売りの成行注文を出します。')
            market_order()
            time.sleep(10)

            check_positions()
            time.sleep(60)

        elif flag['position']['exist'] is True:
        if flag['position']['side'] == 'BUY' and flag['order']['side'] == 'SELL':
            print('決済をして、売りの成行注文を出します。')
            close_order()
            time.sleep(10)

            market_order()
            time.sleep(10)
            check_positions()
            
            time.sleep(60)

        elif flag['position']['side'] == 'SELL' and flag['order']['side'] == 'BUY':
            print('決済をして、買いの成行注文を出します。')
            close_order()
            time.sleep(10)

            market_order()
            time.sleep(10)
            check_positions()
            
            time.sleep(60)

    time.sleep(60)

import time
from datetime import datetime
from datetime import timedelta
import requests
import hmac
import hashlib
import json
import pandas as pd

apiKey = 'YOUR_API_KEY'
secretKey = 'YOUR_SECRET'

# 設定項目
symbol = 'BTC_JPY'  # 使用する通貨ペア
interval = '1hour'  # 使用する時間軸
order_size = '0.01'  # 注文数量
MA_short_period = 5  # 移動平均線の短期の日数
MA_long_period = 25  # 移動平均線の長期の日数
get_price_period = 10  # 四本値を取得する日数

flag = {
    'order': {
        'side': '',
    },
    'position': {
        'exist': False,
        'side': None,
        'lot': 0.0,
    },
}

timestamp = '{0}000'.format(int(time.mktime(datetime.now().timetuple())))
            method = 'POST'
            endPoint = 'https://api.coin.z.com/private'
            path = '/v1/order'
            reqBody = {
                'symbol': symbol,
                'side': flag['order']['side'],
                'size': order_size,
                'executionType': 'MARKET'
            }

            text = timestamp + method + path + json.dumps(reqBody)
            sign = hmac.new(bytes(secretKey.encode('ascii')), bytes(text.encode('ascii')), hashlib.sha256).hexdigest()

            headers = {
                'API-KEY': apiKey,
                'API-TIMESTAMP': timestamp,
                'API-SIGN': sign
            }
            
            res = requests.post(endPoint + path, headers=headers, data=json.dumps(reqBody))
            

def get_price_data():
    while True:
        try:
            price_data = []
            response_data = []

            if 0 <= datetime.today().hour <= 5:
                today = datetime.today().date()

            else:
                today = datetime.today().date() + timedelta(days=1)

            for i in range(get_price_period):
                period = i - get_price_period
                date = (today + timedelta(days=period)).strftime('%Y%m%d')
                endPoint = 'https://api.coin.z.com/public'
                path = '/v1/klines?symbol=' + symbol + '&interval=' + interval + '&date=' + date

                response = requests.get(endPoint + path)
                response_data.append(response.json())

            for j in range(len(response_data)):
                for i in response_data[j]['data']:
                    price_data.append({'openTime': i['openTime'],
                                       'openTime_dt': datetime.fromtimestamp(int(i['openTime'][:-3]))
                                      .strftime('%Y/%m/%d %H:%M'),
                                       'open': i['open'],
                                       'high': i['high'],
                                       'low': i['low'],
                                       'close': i['close']})

            print('TIME： ' + str(price_data[-1]['openTime_dt']) + ' H： ' + str(price_data[-1]['high']) + ' L： ' +
                  str(price_data[-1]['low']) + ' C： ' + str(price_data[-1]['close']))

            df_price_data = pd.DataFrame(price_data)

            return df_price_data

        except Exception as e:
            print(f'action=get_price_data error={e}')
            print('10秒後にリトライします')
            time.sleep(10)

# 移動平均線のGC、DCを判定してエントリー注文を出す関数
def entry_signal(df):
    global flag

    df['MA-short'] = df['open'].rolling(MA_short_period).mean()
    df['MA-long'] = df['open'].rolling(MA_long_period).mean()

    if df.iat[-2, 6] < df.iat[-2, 7] and df.iat[-1, 6] > df.iat[-1, 7]:
        flag['order']['side'] = 'BUY'
        print('ゴールデンクロスが発生しました。')

    elif df.iat[-2, 6] > df.iat[-2, 7] and df.iat[-1, 6] < df.iat[-1, 7]:
        flag['order']['side'] = 'SELL'
        print('デッドクロスが発生しました。')

    else:
        flag['order']['side'] = ''

    return flag

# メイン処理
check_positions()

while True:

    price = get_price_data()
    entry_signal(price)

    if flag['position']['exist'] is False:
        if flag['order']['side'] == 'BUY':
            print('買いの成行注文を出します。')
            market_order()
            time.sleep(10)

            check_positions()
            time.sleep(60)

        if flag['order']['side'] == 'SELL':
            print('売りの成行注文を出します。')
            market_order()
            time.sleep(10)

            check_positions()
            time.sleep(60)

        elif flag['position']['exist'] is True:
        if flag['position']['side'] == 'BUY' and flag['order']['side'] == 'SELL':
            print('決済をして、売りの成行注文を出します。')
            close_order()
            time.sleep(10)

            market_order()
            time.sleep(10)
            check_positions()
            
            time.sleep(60)

        elif flag['position']['side'] == 'SELL' and flag['order']['side'] == 'BUY':
            print('決済をして、買いの成行注文を出します。')
            close_order()
            time.sleep(10)

            market_order()
            time.sleep(10)
            check_positions()
            
            time.sleep(60)

    time.sleep(60)

import pandas as pd
import yfinance as yf
import mplfinance as mpf

# 期間の設定
period = 14

# 株価データを取得（例：日経平均株価のデータを使用）
ticker = '^N225'
data = yf.download(ticker, start='2023-01-01', end='2023-12-31')

# 終値の差を取る
df_close = data["Close"]
df_diff = df_close.diff()

# 終値の差が0以下に0を代入する
df_up = df_diff.copy()
df_up[df_up < 0] = 0

# 終値の差が0以上に0を代入する
df_down = df_diff.copy()
df_down[df_down > 0] = 0

# 上昇と下落の移動平均を計算する
df_up_sum = df_up.rolling(window=period).mean()
df_down_sum = df_down.abs().rolling(window=period).mean()

# RSを計算する
df_rs = df_up_sum / df_down_sum

# RSIを計算する
df_rsi = 100 - (100 / (1 + df_rs))

# MACDとシグナルラインをプロットするためのデータ準備
rsi_plot = [mpf.make_addplot(df_rsi, panel=1, color='blue', ylabel='RSI')]

# グラフのプロット
mpf.plot(
    data,
    type='candle',  # ローソク足チャート
    addplot=rsi_plot,
    style='yahoo',
    title='Stock Price with RSI',
    ylabel='Price',
    panel_ratios=(3, 1),  # 上部3:下部1の比率
    volume=False  # 出来高を非表示
)

# 期間の設定
period = 14

# 終値の差を取る
df_close = data["Close"]
df_diff = df_close.diff()

# 終値の差が0以下に0を代入する
df_up = df_diff.copy()
df_up[df_up < 0] = 0

# 終値の差が0以上に0を代入する
df_down = df_diff.copy()
df_down[df_down > 0] = 0

# 上昇と下降の移動平均を計算する
df_up_sum = df_up.rolling(window=period).mean()
df_down_sum = df_down.abs().rolling(window=period).mean()

# RSを計算する
df_rs = df_up_sum / df_down_sum

# RSIを計算する
df_rsi = 100 - (100 / (1 + df_rs))