python - spyder中的RSI使用excel中的数据

Question

所以我有一个包含特定股票数据的 excel 文件。
我的 excel 文件包含大约 2 个月的数据，它以 5 分钟的间隔监控交易的开盘价、收盘价、最高价、最低价和交易量，因此我的文件中有大约 3000 行。
我想每天计算股票的 RSI(或 EMA，如果它更容易的话)，我正在制作一个收集每日数据的汇总表，因此它将我的 3000 多行表转换为只有大约 60 行的表(每行代表一天)。
本质上，我想要某种代码，按日期对 excel 数据进行排序，然后将 RSI 计算为当天的单个值。 RSI 由下式给出:100-(100/(1+RS)) 其中 RS = 上行周期的平均 yield /下行周期的平均损失。
注意:我的 excel 使用“日期时间”，所以每一行的“日期时间”看起来像“2022-03-03 9:30-5:00”，下一行是“2022-03-03 9:35-5:00” ' 等等。所以代码只需要查看日期并忽略我猜的时间。
一些代码可能有助于理解我在寻找什么:
所以在这里我调用我的 excel 文件，我希望代码获取调用的 excel 文件，按日期对数据进行分组，然后使用我上面写的公式计算每天的 RSI。

dat = pd.read_csv('AMD_5m.csv',index_col='Datetime',parse_dates=['Datetime'],
                  date_parser=lambda x: pd.to_datetime(x, utc=True))
dates = backtest.get_dates(dat.index)
#create a summary table
cols = ['Num. Obs.', 'Num. Trade', 'PnL', 'Win. Ratio','RSI'] #add addtional fields if necessary
summary_table = pd.DataFrame(index = dates, columns=cols)
# loop backtest by dates

这是我用来填写汇总表中其他列的代码，我将把我的 SMA(简单移动平均线)函数放在下面。

for d in dates:
    this_dat = dat.loc[dat.index.date==d]
    #find the number of observations in date d
    summary_table.loc[d]['Num. Obs.'] = this_dat.shape[0] 
    #get trading (i.e. position holding) signals
    signals = backtest.SMA(this_dat['Close'].values, window=10)
    #find the number of trades in date d
    summary_table.loc[d]['Num. Trade'] = np.sum(np.diff(signals)==1) 
    #find PnLs for 100 shares
    shares = 100
    PnL = -shares*np.sum(this_dat['Close'].values[1:]*np.diff(signals))
    if np.sum(np.diff(signals))>0:
        #close position at market close
        PnL += shares*this_dat['Close'].values[-1]
    summary_table.loc[d]['PnL'] = PnL
    #find the win ratio
    ind_in = np.where(np.diff(signals)==1)[0]+1
    ind_out = np.where(np.diff(signals)==-1)[0]+1
    num_win = np.sum((this_dat['Close'].values[ind_out]-this_dat['Close'].values[ind_in])>0)
    if summary_table.loc[d]['Num. Trade']!=0:
        summary_table.loc[d]['Win. Ratio'] = 1. *num_win/summary_table.loc[d]['Num. Trade']

这是我计算简单移动平均线的函数。有人告诉我尝试将其调整为 RSI 或 EMA(指数移动平均线)。显然，为 EMA 调整它并不太麻烦，但我无法弄清楚。

def SMA(p,window=10,signal_type='buy only'):
    #input price "p", look-back window "window",
    #signal type = buy only (default) --gives long signals, sell only --gives sell signals, both --gives both long and short signals
    #return a list of signals = 1 for long position and -1 for short position
    signals = np.zeros(len(p))
    if len(p)<window:
        #no signal if no sufficient data
        return signals
    sma = list(np.zeros(window)+np.nan) #the first few prices does not give technical indicator values 
    sma += [np.average(p[k:k+window]) for k in np.arange(len(p)-window)]
    for i in np.arange(len(p)-1):
        if np.isnan(sma[i]):
            continue #skip the open market time window
        if sma[i]<p[i] and (signal_type=='buy only' or signal_type=='both'):
            signals[i] = 1
        elif sma[i]>p[i] and (signal_type=='sell only' or signal_type=='both'):
            signals[i] = -1
    return signals

Answer 1

我有两个解决方案。一种是遍历每个组，然后将相关数据添加到summary_table ，另一种是计算整个系列并设置RSI列这样。
我首先重新创建了数据:

import yfinance
import pandas as pd

# initially created similar data through yfinance,
# then copied this to Excel and changed the Datetime column to match yours.
df = yfinance.download("AAPL", period="60d", interval="5m")
# copied it and read it as a dataframe
df = pd.read_clipboard(sep=r'\s{2,}', engine="python")
df.head()
#                 Datetime        Open        High         Low       Close   Adj Close   Volume
#0  2022-03-03 09:30-05:00  168.470001  168.910004  167.970001  168.199905  168.199905  5374241
#1  2022-03-03 09:35-05:00  168.199997  168.289993  167.550003  168.129898  168.129898  1936734
#2  2022-03-03 09:40-05:00  168.119995  168.250000  167.740005  167.770004  167.770004  1198687
#3  2022-03-03 09:45-05:00  167.770004  168.339996  167.589996  167.718094  167.718094  2128957
#4  2022-03-03 09:50-05:00  167.729996  167.970001  167.619995  167.710007  167.710007   968410

然后我格式化数据并创建 summary_table :

df["date"] = pd.to_datetime(df["Datetime"].str[:16], format="%Y-%m-%d %H:%M").dt.date

# calculate percentage change from open and close of each row
df["gain"] = (df["Close"] / df["Open"]) - 1

# your summary table, slightly changing the index to use the dates above
cols = ['Num. Obs.', 'Num. Trade', 'PnL', 'Win. Ratio','RSI'] #add addtional fields if necessary
summary_table = pd.DataFrame(index=df["date"].unique(), columns=cols)

选项1:

# loop through each group, calculate the average gain and loss, then RSI
for grp, data in df.groupby("date"):
    # average gain for gain greater than 0
    average_gain = data[data["gain"] > 0]["gain"].mean()
    # average loss for gain less than 0
    average_loss = data[data["gain"] < 0]["gain"].mean()
    
    # add to relevant cell of summary_table
    summary_table["RSI"].loc[grp] = 100 - (100 / (1 + (average_gain / average_loss)))

选项 2:

# define a function to apply in the groupby
def rsi_calc(series):
    avg_gain = series[series > 0].mean()
    avg_loss = series[series < 0].mean()
    
    return 100 - (100 / (1 + (avg_gain / avg_loss)))

summary_table["RSI"] = df.groupby("date")["gain"].apply(lambda x: rsi_calc(x))

输出(每个相同):

summary_table.head()
#           Num. Obs. Num. Trade  PnL Win. Ratio          RSI
#2022-03-03       NaN        NaN  NaN        NaN  -981.214015
#2022-03-04       NaN        NaN  NaN        NaN   501.950956
#2022-03-07       NaN        NaN  NaN        NaN  -228.379066
#2022-03-08       NaN        NaN  NaN        NaN -2304.451654
#2022-03-09       NaN        NaN  NaN        NaN  -689.824739