PWMA_10: Pot Weighted Moving Average,位移加权移动平均线,是一种加权移动平均线。 RMA_10: Rolling Moving Average,滚动移动平均线,一种常见的移动平均线。 SINWMA_14: Sine Weighted Moving Average,正弦权重移动平均线,一种特殊的加权平均方法。 SMA_10: Simple Moving Average,简单移动平均线,最常见的移动平...
Linear Regression Moving Average (LINREG). This is a simplified version of a Standard Linear Regression. LINREG is a rolling regression of one variable. A Standard Linear Regression is between two or more variables. Source: TA Lib Calculation: Default Inputs: length=14 x = [1, 2, ..., n...
rolling(5).mean() 数据修改 # 删除最后一行 df = df.drop(labels=df.shape[0]-1) # 添加一行数据['Perl',6.6] row = {'grammer':'Perl','popularity':6.6} df = df.append(row,ignore_index=True) # 某列小数转百分数 df.style.format({'data': '{0:.2%}'.format}) # 反转行 df.iloc[...
symbols based on a linear combination of the two."""# Use the pandas Ordinary Least Squares method to fit a rolling# linear regression between the two closing price time series#print "Fitting the rolling Linear Regression..."model = pd.ols(y=pairs['%s_close'% symbols[0].lower()], x=...
df['收盘价(元)'].rolling(5).mean() 数据修改 # 删除最后一行 df = df.drop(labels=df.shape[0]-1) # 添加一行数据['Perl',6.6] row = {<!-- -->'grammer':'Perl','popularity':6.6} df = df.append(row,ignore_index=True)
df['收盘价(元)'].rolling(5).mean() 数据修改# 删除最后一行 df = df.drop(labels=df.shape[0]-1) # 添加一行数据['Perl',6.6] row = {'grammer':'Perl','popularity':6.6} df = df.append(row,ignore_index=True) # 某列小数转百分数 ...
rolling(5).mean() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 数据修改 # 删除最后一行df = df.drop(labels=df.shape[0]-1)# 添加一行数据['Perl',6.6]row = {'grammer':'Perl','popularity':6.6}df = df.append(row,ignore_index=True)# 某列小数转百分数df.style.format({'data'...
import pandas as pdimport yfinance as yf# 获取股票数据data = yf.download("AAPL", start="2020-01-01", end="2022-01-01")# 计算移动平均线data['MA50'] = data['Close'].rolling(window=50).mean()# 绘制股票价格与移动平均线data[['Close', 'MA50']].plot() ...
参见我的代码: def lr_r2_Sklearn(data): data = np.array(data) X = pd.Series(list(range(0,len(data),1))).values.reshape(-1,1) Y = data.reshape(-1,1) regressor = LinearRegression() regressor.fit(X,Y) return(regressor.score(X,Y)) r2_rolling = df[[ 浏览29提问于2020-06-26...
rolling(5).mean() 数据修改 代码语言:javascript 复制 # 删除最后一行 df = df.drop(labels=df.shape[0]-1) # 添加一行数据['Perl',6.6] row = {'grammer':'Perl','popularity':6.6} df = df.append(row,ignore_index=True) # 某列小数转百分数 df.style.format({'data': '{0:.2%}'....