2 기술적 분석¶

Fundamental Analysis & Back Testing

1 Candle Chart 그리기¶

기술적 지표 분석을 위한 데이터 수집 및 분석하기

plotly 를 활용하여 SMA, EMA 보조지표 그리기

In [1]:

%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
import matplotlib.pyplot as plt
from matplotlib.font_manager import fontManager as fm

plt.style.use('seaborn') # 'seaborn-colorblind'
plt.rc('axes', unicode_minus=False)
warnings.simplefilter(action='ignore', category=FutureWarning)
font_list_check  = ['D2Coding', 'NanumGothicCoding', 'NanumGothic']

for font in font_list_check:
    font_counts = [ f  for f in font_list_check 
        if font in [_.name for _ in fm.ttflist]]
    if len(font_counts) > 1: 
        print(f"found : {font}"); break
        
plt.rc('font', family=font) # 폰트 적용하기

found : D2Coding

In [2]:

DATA_FILENAME = 'data/stock-twitter.pkl'
import pandas as pd
import yfinance as yf
try:
    df_twiter = pd.read_pickle(DATA_FILENAME)
except FileNotFoundError:
    df_twiter = yf.download('TWTR', start='2020-01-01', end='2022-12-31', progress=False)
    df_twiter.to_pickle(DATA_FILENAME)
df_twiter.head(3)

Out[2]:

	Open	High	Low	Close	Adj Close	Volume
Date
2019-12-31	31.860001	32.320000	31.770000	32.049999	32.049999	11050600
2020-01-02	32.310001	32.500000	31.959999	32.299999	32.299999	10721100
2020-01-03	31.709999	32.099998	31.260000	31.520000	31.520000	14429500

In [3]:

# plotly 활성화 하기
import cufflinks as cf
from plotly.offline import iplot, init_notebook_mode
cf.set_config_file(world_readable=True, theme='pearl', offline=True) # set up settings (run it once)
init_notebook_mode()

# 수집한 데이터를 활용하여 그림 그리기 (1.캔들차트, 2.거래량, 3,4.이동평균 곡선)
# SMA : 단순 이동 평균 (Simple Moving Average)
# EMA : 지수 이동 평균 (Exponential Moving Average)
qf = cf.QuantFig(df_twiter, title='Twitter Stock Price', legend='top', name='twitter')
qf.add_sma(periods=20, column='Close', color='red')
qf.add_ema(periods=20, color='green')
qf.add_volume()
qf.iplot()

2 Back Testing 시작하기¶

BackTesting with PANDAS
다중 데이터 BackTesting
matplotlib Warning Error Fix
단순 이동평균 전략등 다양한 전략을 간편하게 Simulation 가능
보조적인 기술적 지표를 활용한 매매분석에 용이

01 Loading the DataSet¶

In [4]:

DATA_FILENAME = 'data/stock-apple.pkl'
import pandas as pd
import yfinance as yf
try:
    data_csv = pd.read_pickle(DATA_FILENAME)
except FileNotFoundError:
    data_csv = yf.download('TWTR', start='2020-01-01', end='2022-12-31', progress=False)
    data_csv.to_pickle(DATA_FILENAME)
data_csv.head(3)

Out[4]:

	Open	High	Low	Close	Adj Close	Volume
Date
2019-12-31	31.860001	32.320000	31.770000	32.049999	32.049999	11050600
2020-01-02	32.310001	32.500000	31.959999	32.299999	32.299999	10721100
2020-01-03	31.709999	32.099998	31.260000	31.520000	31.520000	14429500

02 Converting The Price DataSet¶

loading from CSV to Pandas DataFrame of the BackTrader DataSet.

https://www.backtrader.com/docu/datafeed/

In [5]:

# Back Trading 전략에 DataFrame 데이터로 Simulation 합니다.
import backtrader as bt
data_stock = bt.feeds.PandasData(dataname = data_csv)
type(data_stock)

Out[5]:

backtrader.feeds.pandafeed.PandasData

3 1개 기준선을 활용한 Back Testing 1¶

종가가 20일 SMA 보다 높으면 매수를 한다
종가가 20일 SMA 보다 낮으면 보유한 주식을 매도한다
주식 매매는 1주만 허용 된다

01 Strategy SIGNAL Class¶

SMA Signal's Class 를 정의하고, BackTesting 진행한다

In [6]:

# Signal Class 를 정의합니다.
# => 20일 이동평균선 전략을 정의 합니다.
class SmaSignal(bt.Signal):
    params = (('period', 20), )
    
    def __init__(self):
        self.lines.signal = self.data - bt.ind.SMA(period=self.p.period)

In [7]:

# Back Testing 을 설정에 추가합니다
cerebro = bt.Cerebro(stdstats=False)
cerebro.adddata(data_stock) # Add the data feed 
cerebro.broker.setcash(1000.0)
cerebro.add_signal(bt.SIGNAL_LONG, SmaSignal)
cerebro.addobserver(bt.observers.BuySell)
cerebro.addobserver(bt.observers.Value)

# Back Testing 실행 결과를 계산 합니다.
print(f'Starting Portfolio Value : {cerebro.broker.getvalue():.2f}')
cerebro.run()
print(f'Final Portfolio Value: {cerebro.broker.getvalue():.2f}')

Starting Portfolio Value : 1000.00
Final Portfolio Value: 1035.30

02 Visualization¶

In [8]:

plt.rcParams['figure.dpi'] = 80
plt.rcParams['figure.figsize'] = [20, 6]
cerebro.plot(iplot=False, volume=False)
plt.show()

4 2개의 이동평균을 활용한 Back Testing¶

단기 (10일) SMA 가 장기(30일) 보다 높아질 때 매수한다
장기 (30일) SMA 가 단기(10일) 보다 높아질 때 매도한다

01 Strategy SIGNAL Class¶

SMA Signal's Class 를 정의하고, BackTesting 진행한다

In [9]:

import backtrader as bt

# 이동평균선 전략을 정의 합니다.
class SmaCross(bt.Strategy): 
    params = dict(
        pfast=10, # fast moving average 
        pslow=30) # slow moving average 
    
    def __init__(self):
        sma1 = bt.ind.SMA(period=self.p.pfast)
        sma2 = bt.ind.SMA(period=self.p.pslow)
        self.crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal 
        
    def next(self): 
        if not self.position:      # not in the market 
            if self.crossover > 0: # if fast crosses slow to the upside 
                self.buy()         # enter long (매수 진행한다)
                
        elif self.crossover < 0: # in the market & cross to the downside 
            self.close()         # close long position (보유시 매도를 진행한다)

In [10]:

# 위 Back Trading 전략에 DataFrame 데이터로 Simulation 합니다.
cerebro = bt.Cerebro()        # create a "Cerebro" engine instance 
cerebro.adddata(data_stock)   # Add the data feed 
cerebro.addstrategy(SmaCross) # Add the trading strategy

# backtest 를 실행한다.
print(f'Starting Portfolio Value : {cerebro.broker.getvalue():.2f}')
cerebro.run() # run it all 
print(f'Final Portfolio Value: {cerebro.broker.getvalue():.2f}')

Starting Portfolio Value : 10000.00
Final Portfolio Value: 10010.79

02 Visualization¶

In [11]:

plt.rcParams['figure.dpi'] = 300
plt.rcParams['figure.figsize'] = [20, 6]
cerebro.plot(iplot=False, volume=False)
plt.show()

5 1개 기준선을 활용한 Back Testing2¶

종가가 20일 SMA 보다 높으면 매수를 한다
종가가 20일 SMA 보다 낮으면 보유한 주식을 매도한다
주식 매매는 1주만 허용 된다

01 Strategy SIGNAL Class¶

SMA Signal's Class 를 정의하고, BackTesting 진행한다
매수, 매도 실행시 세부조건을 설정한다

In [12]:

class SmaStrategy(bt.Strategy):
    params = (('ma_period', 20), )

    def __init__(self):
        # keep track of pending orders/buy price/buy commission
        self.data_close = self.datas[0].close  # Series 에서 close 추출
        self.order = None
        self.price = None
        self.comm = None
        self.sma = bt.ind.SMA( # add a simple moving average indicator
            self.datas[0], period=self.params.ma_period)
        
    def log(self, txt):
        dt = self.datas[0].datetime.date(0).isoformat()
        print(f'{dt}, {txt}')

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # order already submitted/accepted - no action required
            return

        # report executed order
        if order.status in [order.Completed]:
            if order.isbuy():
                self.log(f'BUY EXECUTED --- Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f}, Commission: {order.executed.comm:.2f}')
                self.price = order.executed.price
                self.comm = order.executed.comm
            else:
                self.log(f'SELL EXECUTED --- Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f}, Commission: {order.executed.comm:.2f}')

        # report failed order
        elif order.status in [
            order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Failed')

        # set no pending order
        self.order = None

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log(f'OPERATION RESULT --- Gross: {trade.pnl:.2f}, Net: {trade.pnlcomm:.2f}')

    def next(self):
        # do nothing if an order is pending
        if self.order:
            return

        # check if there is already a position
        if not self.position:
            # buy condition
            if self.data_close[0] > self.sma[0]:
                self.log(f'BUY CREATED --- Price: {self.data_close[0]:.2f}')
                self.order = self.buy()
        else:
            # sell condition
            if self.data_close[0] < self.sma[0]:            
                self.log(f'SELL CREATED --- Price: {self.data_close[0]:.2f}')
                self.order = self.sell()

In [13]:

cerebro = bt.Cerebro(stdstats = False)
cerebro.adddata(data_stock) # Add the data feed 
cerebro.broker.setcash(1000.0)
cerebro.addstrategy(SmaStrategy)
cerebro.addobserver(bt.observers.BuySell)
cerebro.addobserver(bt.observers.Value)

print(f'Starting Portfolio Value: {cerebro.broker.getvalue():.2f}')
cerebro.run()
print(f'Final Portfolio Value: {cerebro.broker.getvalue():.2f}')

Starting Portfolio Value: 1000.00
2020-01-29, BUY CREATED --- Price: 33.63
2020-01-30, BUY EXECUTED --- Price: 32.86, Cost: 32.86, Commission: 0.00
2020-01-31, SELL CREATED --- Price: 32.48
2020-02-03, SELL EXECUTED --- Price: 32.56, Cost: 32.86, Commission: 0.00
2020-02-03, OPERATION RESULT --- Gross: -0.30, Net: -0.30
2020-02-04, BUY CREATED --- Price: 33.96
2020-02-05, BUY EXECUTED --- Price: 34.00, Cost: 34.00, Commission: 0.00
2020-02-25, SELL CREATED --- Price: 35.21
2020-02-26, SELL EXECUTED --- Price: 35.28, Cost: 34.00, Commission: 0.00
2020-02-26, OPERATION RESULT --- Gross: 1.28, Net: 1.28
2020-04-07, BUY CREATED --- Price: 25.61
2020-04-08, BUY EXECUTED --- Price: 26.79, Cost: 26.79, Commission: 0.00
2020-04-21, SELL CREATED --- Price: 25.75
2020-04-22, SELL EXECUTED --- Price: 27.00, Cost: 26.79, Commission: 0.00
2020-04-22, OPERATION RESULT --- Gross: 0.21, Net: 0.21
2020-04-22, BUY CREATED --- Price: 28.44
2020-04-23, BUY EXECUTED --- Price: 28.43, Cost: 28.43, Commission: 0.00
2020-05-06, SELL CREATED --- Price: 27.68
2020-05-07, SELL EXECUTED --- Price: 28.08, Cost: 28.43, Commission: 0.00
2020-05-07, OPERATION RESULT --- Gross: -0.35, Net: -0.35
2020-05-07, BUY CREATED --- Price: 28.77
2020-05-08, BUY EXECUTED --- Price: 29.09, Cost: 29.09, Commission: 0.00
2020-05-13, SELL CREATED --- Price: 28.13
2020-05-14, SELL EXECUTED --- Price: 27.83, Cost: 29.09, Commission: 0.00
2020-05-14, OPERATION RESULT --- Gross: -1.26, Net: -1.26
2020-05-14, BUY CREATED --- Price: 28.56
2020-05-15, BUY EXECUTED --- Price: 28.30, Cost: 28.30, Commission: 0.00
2020-06-19, SELL CREATED --- Price: 33.41
2020-06-22, SELL EXECUTED --- Price: 33.57, Cost: 28.30, Commission: 0.00
2020-06-22, OPERATION RESULT --- Gross: 5.27, Net: 5.27
2020-07-07, BUY CREATED --- Price: 32.99
2020-07-08, BUY EXECUTED --- Price: 33.20, Cost: 33.20, Commission: 0.00
2020-09-08, SELL CREATED --- Price: 38.19
2020-09-09, SELL EXECUTED --- Price: 38.85, Cost: 33.20, Commission: 0.00
2020-09-09, OPERATION RESULT --- Gross: 5.65, Net: 5.65
2020-09-18, BUY CREATED --- Price: 40.15
2020-09-21, BUY EXECUTED --- Price: 39.43, Cost: 39.43, Commission: 0.00
2020-09-21, SELL CREATED --- Price: 39.90
2020-09-22, SELL EXECUTED --- Price: 40.37, Cost: 39.43, Commission: 0.00
2020-09-22, OPERATION RESULT --- Gross: 0.94, Net: 0.94
2020-09-22, BUY CREATED --- Price: 42.73
2020-09-23, BUY EXECUTED --- Price: 43.88, Cost: 43.88, Commission: 0.00
2020-10-30, SELL CREATED --- Price: 41.36
2020-11-02, SELL EXECUTED --- Price: 41.15, Cost: 43.88, Commission: 0.00
2020-11-02, OPERATION RESULT --- Gross: -2.73, Net: -2.73
2020-11-20, BUY CREATED --- Price: 44.68
2020-11-23, BUY EXECUTED --- Price: 44.90, Cost: 44.90, Commission: 0.00
2021-01-07, SELL CREATED --- Price: 52.33
2021-01-08, SELL EXECUTED --- Price: 52.50, Cost: 44.90, Commission: 0.00
2021-01-08, OPERATION RESULT --- Gross: 7.60, Net: 7.60
2021-01-28, BUY CREATED --- Price: 51.57
2021-01-29, BUY EXECUTED --- Price: 51.02, Cost: 51.02, Commission: 0.00
2021-03-04, SELL CREATED --- Price: 66.75
2021-03-05, SELL EXECUTED --- Price: 67.67, Cost: 51.02, Commission: 0.00
2021-03-05, OPERATION RESULT --- Gross: 16.65, Net: 16.65
2021-04-06, BUY CREATED --- Price: 67.00
2021-04-07, BUY EXECUTED --- Price: 66.64, Cost: 66.64, Commission: 0.00
2021-04-20, SELL CREATED --- Price: 65.70
2021-04-21, SELL EXECUTED --- Price: 65.73, Cost: 66.64, Commission: 0.00
2021-04-21, OPERATION RESULT --- Gross: -0.91, Net: -0.91
2021-04-21, BUY CREATED --- Price: 67.33
2021-04-22, BUY EXECUTED --- Price: 67.42, Cost: 67.42, Commission: 0.00
2021-04-22, SELL CREATED --- Price: 64.31
2021-04-23, SELL EXECUTED --- Price: 65.72, Cost: 67.42, Commission: 0.00
2021-04-23, OPERATION RESULT --- Gross: -1.70, Net: -1.70
2021-05-24, BUY CREATED --- Price: 57.06
2021-05-25, BUY EXECUTED --- Price: 57.35, Cost: 57.35, Commission: 0.00
2021-07-16, SELL CREATED --- Price: 66.41
2021-07-19, SELL EXECUTED --- Price: 65.24, Cost: 57.35, Commission: 0.00
2021-07-19, OPERATION RESULT --- Gross: 7.89, Net: 7.89
2021-07-21, BUY CREATED --- Price: 69.54
2021-07-22, BUY EXECUTED --- Price: 68.64, Cost: 68.64, Commission: 0.00
2021-07-26, SELL CREATED --- Price: 68.69
2021-07-27, SELL EXECUTED --- Price: 68.32, Cost: 68.64, Commission: 0.00
2021-07-27, OPERATION RESULT --- Gross: -0.32, Net: -0.32
2021-07-28, BUY CREATED --- Price: 69.96
2021-07-29, BUY EXECUTED --- Price: 69.50, Cost: 69.50, Commission: 0.00
2021-08-03, SELL CREATED --- Price: 68.63
2021-08-04, SELL EXECUTED --- Price: 68.68, Cost: 69.50, Commission: 0.00
2021-08-04, OPERATION RESULT --- Gross: -0.82, Net: -0.82
2021-08-05, BUY CREATED --- Price: 69.28
2021-08-06, BUY EXECUTED --- Price: 69.30, Cost: 69.30, Commission: 0.00
2021-08-06, SELL CREATED --- Price: 67.37
2021-08-09, SELL EXECUTED --- Price: 67.50, Cost: 69.30, Commission: 0.00
2021-08-09, OPERATION RESULT --- Gross: -1.80, Net: -1.80
Final Portfolio Value: 1035.30

02 Visualization¶

https://community.backtrader.com/topic/2881/cannot-import-name-warnings-from-matplotlib-dates

In [14]:

cerebro.plot(iplot=False)
plt.show()