class UserInputs:
# user inputs
ticker = input('enter stock ticker')
print(mpf.available_styles())
timeframe = input('enter a timeframe:')
long_ma = int(
input('enter length for long SMA (used taken for derivative)'))
short_ma = int(input('enter length for short SMA'))
# load stock data
start = dt.datetime(2000, 1, 1)
end = dt.datetime.now()
df = web.DataReader(ticker, 'yahoo', start, end)
def plotKLine(data, add_plot):
data['Time'] = pd.to_datetime(data['Time'], unit='ms')
data.index = data['Time']
data.rename(columns={
'open': 'Open',
'close': 'Close',
'high': 'High',
'low': 'Low',
'Time': 'Date'
},
inplace=True)
# mpf.plot(data, type='candle',style='yahoo',figratio=(2,1), figsize=(20,11),mav=(2, 5, 10))
mpf.plot(data,
type='candle',
style='yahoo',
addplot=add_plot,
figratio=(2, 1),
figsize=(20, 11))
print(mpf.available_styles())
# REF: https://kknews.cc/code/j5kgzae.html
# 時間序列（time series）簡單的說就是各時間點上形成的數值序列，時間序列（time series）分析就是通過觀察歷史數據預測未來的值。
# 這個函數主要是返回固定頻率的時間索引
pd.date_range(start='2019-1-09', end='2019-1-31')
# index_col : 做為索引的欄位(以數值或字串表示)
# parse_dates : 格式化日期欄位(使為索引的欄位)
#df = pd.read_csv('stocks.csv',index_col=0,parse_dates=True)
#df = pd.read_csv('stocks.csv',index_col=1,parse_dates=True)
#df = pd.read_csv('stocks.csv',index_col='Date',parse_dates=True)
# 下面的註解是 for windows
#df = pd.read_csv("..\\Files\\2002.csv", \
df = pd.read_csv("..//Files//2002.csv", \
header = 0, \
names = ['date', 'open', 'high', 'low', 'close', 'volume', \
'marginTrading', 'shortSelling'], \
index_col = 'date', \
parse_dates = True)
# 支援K線圖的種類
mpf.available_styles()
mpf.plot(df, type = 'candle', \
title = 'iron 2002', \
style = 'charles', \
volume = True, \
ylabel= 'Price ($)', \
ylabel_lower = 'Ha ha Vol')
import pandas as pd
import numpy as np
import mplfinance as mpf
import matplotlib.pyplot as plt
print(mpf.available_styles())
daily = pd.read_csv(r'examples_data\SP500_NOV2019_Hist.csv',
index_col=0,
parse_dates=True)
print(daily.head())
daily.index.name = "Date"
# 简单定义：
# 1.如果收盘价比开盘价低5，做黄色标记
# 2.如果开盘价比收盘价高5，做红色标记
red_list = []
yellow_list = []
for _, row in daily.iterrows():
if row["Open"] - row["Close"] > 5:
red_list.append(row["Close"])
else:
red_list.append(np.NaN)
if row["Close"] - row["Open"] > 5:
yellow_list.append(row["Open"])
else:
yellow_list.append(np.NaN)
add_plot = [
| |
------ 종가(Close)
|
| 저가(Low) """
#%%
import mplfinance as mpf
kospi = yf.download('^KS11', start="2019-10-01", end="2020-12-31")
#%% 기본차트
mpf.plot(kospi) # type을 지정하지 않으면 OHLC형태의 차트가 출력
mpf.plot(kospi, type='candle', title='KOSPI', ylabel='Price')
mpf.available_styles() # mplfinance plot에서 가능한 스타일 확인
mpf.plot(kospi, type='candle', style='sas', title='KOSPI', ylabel='Price')
#%% 거래량(Volume) 추가
mpf.plot(kospi,
type='candle',
style='sas',
title='KOSPI',
ylabel='Price',
volume=True) # NOTE 볼륨을 자동으로 가져와서 그려준다.
#%%
mpf.plot(kospi,
type='candle',
style='sas',
title='KOSPI',
ylabel='Price',