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siamchart_csv.py
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siamchart_csv.py
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import os
import numpy as np
import pandas as pd
import shutil
import matplotlib.pyplot as plt
from os import listdir, makedirs
from os.path import isfile, join, exists
from matplotlib.finance import date2num
from matplotlib.dates import DateFormatter, WeekdayLocator,DayLocator, MONDAY
from matplotlib.finance import candlestick_ohlc
from tqdm import tqdm
# Requirement
# 1) install Anaconda: https://www.continuum.io/downloads
# 2) pip install tqdm
# 3) download EOD file: http://siamchart.com/stock/ (Must register to login)
def getFileNameInDir(path):
onlyFiles = [ join(path,f) for f in listdir(path) if isfile(join(path, f)) ]
return onlyFiles
def getHeaderFile(eodFiles):
for f in eodFiles:
df = pd.read_csv(f)
return df.columns.values # read a header file from first file
def getStockData(eodFiles, selectedSmbol = []):
dict = {} # empty dictionary
totalFiles = len(eodFiles)
for i in tqdm(range(totalFiles), ascii=True, desc='Reading EOD files'):
f = eodFiles[i]
df = pd.read_csv(f)
total_row = len(df.index)
all_data = df.values;
#range(start, stop, step)
for row in range(total_row-1, -1, -1): # reverse form range(0, total_row)
symbol = all_data[row][0] # name of stock in the first column
if len(selectedSmbol)!=0 :
if not symbol in selectedSmbol: continue
if symbol == "COM7": symbol = "COM7_" # fix bug for this symbol only
current_row = all_data[row]
if symbol in dict: # There are many symbol data in dictionary
dict[symbol].append(current_row) # append new data to old data
else: # no symbol data in dictionary
dict[symbol] = [current_row]
return dict
def clearDir(dirPath):
if exists(dirPath):
shutil.rmtree(dirPath)
makedirs(dirPath)
def changeName(name):
"""
I change this column name ["<OPEN>", "<HIGH>", "<LOW>", "<CLOSE>", "<VOL>"]
"""
if name in ["<OPEN>", "<HIGH>", "<LOW>", "<CLOSE>"]:
# Frist charector is upper case
name = name.replace('<', '').replace('>', '')
#name = name[0] + name[1:].lower()
elif name in ["<VOL>"]:
#name = name.replace("<VOL>", "Volume")
name = name.replace("<VOL>", "VOLUME")
elif name in ["<DTYYYYMMDD>"]:
#name = name.replace("<DTYYYYMMDD>", "Date")
name = name.replace("<DTYYYYMMDD>", "DATE")
return name
DIR_CURRENT = os.path.dirname(__file__)
DIR_SEC_CSV = "sec_csv"
# download: http://siamchart.com/stock/ (Must register to login)
#EOD_file = "D:/MyProject/Big-datasets/data_stock/set-archive_EOD_UPDATE"
EOD_file = "set-archive_EOD_UPDATE"
def createSymbolCSV(start_idex, outputPath=DIR_SEC_CSV):
eodFiles = getFileNameInDir(EOD_file)
eodFiles = eodFiles[-1 * start_idex:] # select files at latest N days
outputPath = join(DIR_CURRENT,outputPath)
clearDir(outputPath) # delete old files
eodFiles = [ join(DIR_CURRENT,file) for file in eodFiles]
dataStock = getStockData(eodFiles)
headers = getHeaderFile(eodFiles) # Read a header in CSV files
columnNames = { index:changeName(value) for index, value in enumerate(headers)}
# write all data to csv files and separate file name be followed by symbol names of securities
itemList = list(dataStock.items())
allItem = len(itemList)
assert allItem == len(dataStock.items())
for i in tqdm(range(allItem), ascii=True, desc='Writing CSV files'):
key, allRow = itemList[i]
df = pd.DataFrame(allRow)
df.rename(columns=columnNames, inplace=True) # change column names in data frame: convert from number to symbol names
df.drop('<TICKER>', axis=1, inplace=True) # remove column
fileName = "{}.csv".format(join(outputPath, key))
df.to_csv(fileName, index = False) # write data into CSV file (without index)
def load_OHLCV(symbol, dates,
#column_names=['Open', 'High', 'Low', 'Close', 'Volume'],
column_names=['OPEN', 'HIGH', 'LOW', 'CLOSE', 'VOLUME'],
base_dir=DIR_SEC_CSV):
#if 'Date' not in column_names:
# column_names = np.append(['Date'],column_names)
if 'DATE' not in column_names:
column_names = np.append(['DATE'],column_names)
base_dir = join(DIR_CURRENT,base_dir)
csv_file = os.path.join(base_dir, "{}.csv".format(symbol))
df_csv = pd.read_csv(csv_file, index_col='DATE',
parse_dates=True, usecols=column_names,
na_values=['nan'])
"""Read securities data for given symbols from CSV files."""
if dates is None:
dates = df_csv.index
df_main = pd.DataFrame(index=dates) # empty data frame that has indexs as dates
df_main = df_main.join(df_csv)
df_main = df_main.dropna(0)
return df_main
def loadStockQuotes(symbol, dates):
#col_names=['Open', 'Close', 'High', 'Low']
col_names = ['OPEN', 'HIGH', 'LOW', 'CLOSE']
df = load_OHLCV(symbol, dates, column_names=col_names, base_dir=DIR_SEC_CSV)
#quotes = [ (date, open, close, high, low), .....]
quotes = df.to_records(convert_datetime64=True).tolist()
quotes = [ (date2num(d), o, c, h, l) for d,o,c,h,l in quotes ]
return quotes
def loadManySymbols(symbols, dates, column_name, base_dir):
"""Read securities data for given symbols from CSV files."""
df = pd.DataFrame(index=dates) # empty data frame that has indexs as dates
if 'SET' not in symbols: # add SET for reference, if absent
symbols = np.append(['SET'],symbols)
base_dir = join(DIR_CURRENT,base_dir)
for symbol in symbols:
# read CSV file path given symbol.
csv_file = os.path.join(base_dir, symbol + '.csv')
#df_temp = pd.read_csv(csv_file, index_col='Date',
#parse_dates=True, usecols=['Date', column_name], na_values=['nan'])
df_temp = pd.read_csv(csv_file, index_col='DATE',
parse_dates=True, usecols=['DATE', column_name],
na_values=['nan'])
df_temp = df_temp.rename(columns={column_name: symbol})
df = df.join(df_temp) # left join by default
if symbol == 'SET': # drop dates SET did not trade (nan values)
df = df.dropna(subset=["SET"])
return df
def loadPriceData(symbol_list, dates, base_dir=DIR_SEC_CSV):
#return loadManySymbols(symbol_list, dates, 'Close', base_dir)
return loadManySymbols(symbol_list, dates, 'CLOSE', base_dir)
# Borrowed code from : http://matplotlib.org/examples/pylab_examples/finance_demo.
def plotCandlestick(symbol, dates, title="Selected data"):
quotes = loadStockQuotes(symbol, dates)
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax, quotes, width=0.6)
ax.xaxis_date()
ax.autoscale_view()
ax.set_title(title)
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.show()
from time import gmtime, strftime
if __name__ == "__main__" :
#Create CSV files for securities
print()
print("----------------------------------------------")
print("-------------Creating csv files---------------")
print("----------------------------------------------")
createSymbolCSV(2000)
#currentDateStr = strftime("%Y-%m-%d %H:%M:%S", gmtime())
startDate = '2017-03-01'
endDate = strftime("%Y-%m-%d", gmtime())
dates = pd.date_range(startDate, endDate)
#load data such as Open, High, Low, Close and Volume
print("Load data: PTT")
df = load_OHLCV("PTT", dates)
print(df.tail())
# plot graph of candle stick
print("\nPlot graph: PTT")
plotCandlestick("PTT", dates, title ="PTT symbol")
# load close prices of many stock
symbols = ["PTT", "AOT", "SCC", "CPALL"]
print("\nLoad close prices of:", symbols)
df = loadPriceData(symbols, dates)
print(df.tail())
# plot graph all close prices
df = df/df.iloc[0,:] # normalized
df.plot()
plt.show()