def load(self):
"""If the config file exists, try to load it"""
if (os.path.exists(self._filename)):
cfgp = getConfigParser(self._filename)
for input in self._inputs:
input.readValue(cfgp)
def load(self):
"""If the config file exists, try to load it"""
if(os.path.exists(self._filename)):
cfgp = getConfigParser(self._filename)
for input in self._inputs:
input.readValue(cfgp)
def selectStocks(year):
parser = getConfigParser()
BASED_DIR = parser.get('happy', 'based_dir')
SOURCE_LOCATION = BASED_DIR + parser.get('happy', 'source_location')
SELECTED_STOCK_LOCATION = BASED_DIR + parser.get(
'happy', 'selected_stock_location')
NUMBER_OF_DAYS = int(parser.get('happy', 'active_days'))
MIN_VOLUME = int(parser.get('happy', 'min_volume'))
MIN_OPEN_PRICE = int(parser.get('happy', 'min_open_price'))
MAX_OPEN_PRICE = int(parser.get('happy', 'max_open_price'))
if len(getCsvFiles(SELECTED_STOCK_LOCATION)) > 0:
sh.rm(glob.glob(SELECTED_STOCK_LOCATION + "*"))
ic("Started selecting stocks...")
csvFiles = getCsvFiles(SOURCE_LOCATION)
# csvFiles = ["AAA.csv","APG.csv"]
selected = []
for file in csvFiles:
df = readFile((SOURCE_LOCATION + "{}").format(file))
df = df.loc[year]
if (len(df) > NUMBER_OF_DAYS) and df.Volume.mean() > MIN_VOLUME and (
df.Open.mean() > MIN_OPEN_PRICE
and df.Open.mean() < MAX_OPEN_PRICE):
df["Short"] = False
df["Long"] = False
df["Categories"] = ""
df["Recommendation"] = ""
df["Action"] = ""
df.to_csv((SELECTED_STOCK_LOCATION + "{}").format(file))
ic("Completed selecting stocks!")
def isOverProfit(df, position, stock, portfolio):
c = df.iloc[position]
if stock in portfolio.index:
parser = getConfigParser()
overcomeProfitRate = float(parser.get('happy', 'over_profit_rate'))
boughtPrice = portfolio.loc[stock].Price
profitThreshold = boughtPrice * (1 + overcomeProfitRate);
# ic("---isOverProfit {} {} {} {}".format(boughtPrice, profitThreshold, c.Open, c.Close))
if min(c.Open, c.Close) < profitThreshold and profitThreshold < max(c.Open, c.Close):
return True;
if isDoji(df, position) and min(c.Open, c.Close) > profitThreshold:
return True;
return False;
def isCutLoss(df, position, stock, portfolio):
c = df.iloc[position]
if stock in portfolio.index:
parser = getConfigParser()
cutLossRate = float(parser.get('happy', 'cut_loss_rate'))
boughtPrice = portfolio.loc[stock].Price
cutLossThreshold = boughtPrice * (1 - cutLossRate)
# ic("---isCutLoss {} {} {} {}".format(boughtPrice, cutLossThreshold, c.Open, c.Close))
if min(c.Open, c.Close) < cutLossThreshold and cutLossThreshold < max(c.Open, c.Close):
return True;
if isDoji(df, position) and max(c.Open, c.Close) < cutLossThreshold:
return True;
return False;
def getTab(filename, *args):
"""Return the correct type EditTab for the specified config file
Argument
filename -- path and name of a config file of a Varberg display
args -- arguments to the EditTab constructor that are not used by getTab
"""
cfgp = getConfigParser(filename)
if(cfgp.has_section('BottomArea')):
return EditTab3(filename, *args)
elif(cfgp.has_section('TopLeftText')):
return EditTab2(filename, *args)
else:
return EditTab1(filename, *args)
def getTab(filename, *args):
"""Return the correct type EditTab for the specified config file
Argument
filename -- path and name of a config file of a Varberg display
args -- arguments to the EditTab constructor that are not used by getTab
"""
cfgp = getConfigParser(filename)
if (cfgp.has_section('BottomArea')):
return EditTab3(filename, *args)
elif (cfgp.has_section('TopLeftText')):
return EditTab2(filename, *args)
else:
return EditTab1(filename, *args)
def updateRec(dailyDf, stock):
parser = getConfigParser()
BASED_DIR = parser.get('happy', 'based_dir')
REC_FULL_LOCATION = BASED_DIR + parser.get('happy', 'rec_full_location')
REC_ACTIONS_LOCATION = BASED_DIR + parser.get('happy',
'rec_actions_location')
try:
# ic("Update rec file for ", stock)
df = readFile(REC_FULL_LOCATION + stock + "_rec.csv")
df.loc[dailyDf.index[0]] = dailyDf.iloc[0]
df.to_csv(REC_FULL_LOCATION + stock + "_rec.csv")
df = df[df.Action.isin(["Sold", "Bought"])]
df.to_csv(REC_ACTIONS_LOCATION + stock + "_rec_actions.csv")
except Exception as ex:
ic(ex)
# ic("Create rec file for ", stock)
dailyDf.to_csv(REC_FULL_LOCATION + stock + "_rec.csv")
dailyDf = dailyDf[dailyDf.Action.isin(["Sold", "Bought"])]
dailyDf.to_csv(REC_ACTIONS_LOCATION + stock + "_rec_actions.csv")
def main():
"""Load the inifile and start the program."""
inidir = os.path.dirname(os.path.abspath(sys.argv[0]))
download = False
if(sys.argv[1].upper() == _displaynames[0]):
unit = varberg1
ininame = _ininames[0]
elif(sys.argv[1].upper() == _displaynames[1]):
unit = varberg2
ininame = _ininames[1]
elif(sys.argv[1].upper() == _displaynames[2]):
unit = varberg3
ininame = _ininames[2]
if(len(sys.argv) > 2):
if(sys.argv[2].upper().startswith(_cache)):
amount = sys.argv[2][len(_cache):]
download = True
elif(sys.argv[1].upper() == _displaynames[3]):
unit = hylte4
ininame = _ininames[3]
else:
print('Please provide a valid display number')
return
inifile = os.path.join(inidir, ininame)
parser = getConfigParser(inifile)
#Workaround for unicode in exefiles
if hasattr(sys,"setdefaultencoding"):
sys.setdefaultencoding("utf-8")
if(parser is not None):
if(download):
if(unit == varberg3):
download3(inidir, parser, amount)
else:
controller = unit.Controller(inidir, parser)
controller.gui.root.mainloop()
def main():
"""Load the inifile and start the program."""
inidir = os.path.dirname(os.path.abspath(sys.argv[0]))
download = False
if (sys.argv[1].upper() == _displaynames[0]):
unit = varberg1
ininame = _ininames[0]
elif (sys.argv[1].upper() == _displaynames[1]):
unit = varberg2
ininame = _ininames[1]
elif (sys.argv[1].upper() == _displaynames[2]):
unit = varberg3
ininame = _ininames[2]
if (len(sys.argv) > 2):
if (sys.argv[2].upper().startswith(_cache)):
amount = sys.argv[2][len(_cache):]
download = True
elif (sys.argv[1].upper() == _displaynames[3]):
unit = hylte4
ininame = _ininames[3]
else:
print('Please provide a valid display number')
return
inifile = os.path.join(inidir, ininame)
parser = getConfigParser(inifile)
#Workaround for unicode in exefiles
if hasattr(sys, "setdefaultencoding"):
sys.setdefaultencoding("utf-8")
if (parser is not None):
if (download):
if (unit == varberg3):
download3(inidir, parser, amount)
else:
controller = unit.Controller(inidir, parser)
controller.gui.root.mainloop()
def analyzeAll(date, files, dailyReports, dailyDf, portfolio, investingMoney,
investingAmount):
for file in files:
parser = getConfigParser()
BASED_DIR = parser.get('happy', 'based_dir')
SELECTED_STOCK_LOCATION = BASED_DIR + parser.get(
'happy', 'selected_stock_location')
REPORT_LOCATION = BASED_DIR + parser.get('happy', 'report_location')
df = readFile((SELECTED_STOCK_LOCATION + "{}").format(file))
df['Action'] = df.Action.astype(str)
df['Categories'] = df.Categories.astype(str)
df['Recommendation'] = df.Recommendation.astype(str)
(dailyDf, portfolio, investingMoney,
investingAmount) = analyzePattern(df, date, file[0:3], dailyDf,
portfolio, investingMoney,
investingAmount)
df.to_csv(SELECTED_STOCK_LOCATION + "{}".format(file))
if len(dailyDf) > 0:
finalDf = functools.reduce(lambda a, b: a.append(b), dailyDf)
dailyReports = dailyReports.append(finalDf)
dailyReports.to_csv(REPORT_LOCATION + "reports.csv", index=True)
portfolio.to_csv(REPORT_LOCATION + "portfolio.csv", index=True)
return (dailyReports, dailyDf, portfolio, investingMoney, investingAmount)
def getRate():
parser = getConfigParser()
return int(parser.get('happy', 'candle_rates'))
from datetime import datetime, timedelta
import common
client = common.getTransmissionClient('cleanup')
print('Running cleanup')
torrents = client.get_torrents()
delay = common.getConfigParser().getint('cleanup', 'delay')
for torrent in torrents:
if torrent.isFinished and not torrent.error and datetime.fromtimestamp(
torrent.doneDate) + timedelta(days=delay) < datetime.now():
print('%s is finished and will be removed' % (torrent.name))
client.remove(torrent.hashString, delete_data=True)
def analyzePattern(df, date, stock, dailyDf, portfolio, investingMoney,
investingAmount):
if not df.loc[str(date)].empty:
positions = df.index.get_loc(str(date))
if len(positions) > 0 and positions[0] < len(df) - 1:
ic(date)
parser = getConfigParser()
MAX_VOLUME = int(parser.get('happy', 'max_volume'))
TRADE_RATE = float(parser.get('happy', 'trade_rate'))
TRADE_STRATEGY = parser.get('happy', 'trade_strategy')
position = positions[0]
df.Short.iloc[position] = df.iloc[position].Change < 1
df.Long.iloc[position] = df.iloc[position].Change >= 5
categorizeCandle(df, position)
recommendDaily(df, position, stock, portfolio)
# ic(df.loc[[df.index[position]]])
updateRec(df.loc[[df.index[position]]], stock)
# Sold the stock on portfolio
if (stock
in portfolio.index) and "Sold" in df.iloc[position].Action:
p = df.iloc[position + 1]
stockVolume = portfolio.loc[stock].Volume
# currentDate = datetime.strptime(date, '%Y-%m-%d')
boughtDate = datetime.strptime(portfolio.loc[stock].Date,
'%Y-%m-%d')
if (date - boughtDate).days < 3:
ic("Waiting...{} {} {}".format(date,
portfolio.loc[stock].Date,
(date - boughtDate).days))
df.Action.iloc[position] = df.Action.iloc[
position] + "| Will Sell as Waiting for T3"
else:
if "Cut Loss" in p.Recommendation:
price = round(df.iloc[position].Close / 2 +
df.iloc[position].Open / 2)
ic("Sold as Cut Loss {}".format(date))
elif "Overcome Profit" in p.Recommendation:
price = round(df.iloc[position].Close / 2 +
df.iloc[position].Open / 2)
ic("Sold as Overcome Profit {}".format(date))
else:
price = getPrice(df.iloc[position], TRADE_STRATEGY)
investingMoney = investingMoney - stockVolume * price
investingAmount = investingAmount + stockVolume * price - getTradeFee(
stockVolume * price, TRADE_RATE)
profit = (price - portfolio.loc[stock].Price) * stockVolume
report = {
"ID": [str(date)[0:10] + "-" + stock],
"Date": [str(date)[0:10]],
"Stock": [stock],
"Action": ["Sell"],
"Volume": [stockVolume],
"Price": [price],
"Value": [stockVolume * price],
"Profit": [profit],
"investingMoney": [investingMoney],
"investingAmount": [investingAmount]
}
stockReportDf = pd.DataFrame.from_dict(report)
stockReportDf.set_index("ID", inplace=True)
dailyDf.append(stockReportDf)
# When allow multiple buys on one stock, the drop statement need to be updated: drop by ID instead of Stock
portfolio.drop(stock, inplace=True)
ic("Sold", stock, stockVolume, price, str(date)[0:10])
# Check stock existing on portfolio without Sold Recommendation:
## 1. Will SELL if overprofit
## 2. Will SELL if
if (stock
in portfolio.index) and ("Sold"
not in df.iloc[position].Action):
if "Cut Loss" in df.iloc[position].Action:
ic("Sell as Cut Loss")
if "Overcome Profit" in df.iloc[position].Action:
ic("Sell as Overcome Profit")
if "Bought" in df.iloc[position].Action and (
stock not in portfolio.index):
# price = df.iloc[position].Open
price = getPrice(df.iloc[position], TRADE_STRATEGY)
stockVolume = getInvestingVolume(price, investingMoney,
investingAmount, MAX_VOLUME)
if stockVolume > 0:
investingMoney = investingMoney + stockVolume * price
investingAmount = investingAmount - stockVolume * price - getTradeFee(
stockVolume * price, TRADE_RATE)
report = {
"ID": [str(date)[0:10] + "-" + stock],
"Date": [str(date)[0:10]],
"Stock": [stock],
"Action": ["Buy"],
"Volume": [stockVolume],
"Price": [price],
"Value": [stockVolume * price],
"Profit": [0],
"investingMoney": [investingMoney],
"investingAmount": [investingAmount]
}
stockReportDf = pd.DataFrame.from_dict(report)
stockReportDf.set_index("ID", inplace=True)
dailyDf.append(stockReportDf)
newStock = pd.DataFrame.from_dict({
"ID": [str(date)[0:10] + "-" + stock],
"Date": [str(date)[0:10]],
"Stock": [stock],
"Price": [price],
"Volume": [stockVolume],
"Value": [stockVolume * price]
})
newStock.set_index("Stock", inplace=True)
portfolio = portfolio.append(newStock)
ic("Bought", stock, stockVolume, price, str(date)[0:10])
return (dailyDf, portfolio, investingMoney, investingAmount)
import pandas as pd
from common import getCsvFiles, readRawFile, readFile, getConfigParser, clearFileContent, clearReports, clearRecFolders
from utils import getInvestingVolume, getTradeFee, selectStocks
from analysis import analyzeAll
from icecream import ic
import logging
from configparser import ConfigParser
parser = getConfigParser()
pd.options.mode.chained_assignment = None
BASED_DIR = parser.get('happy', 'based_dir')
SELECTED_STOCK_LOCATION = BASED_DIR + parser.get('happy', 'selected_stock_location')
REPORT_LOCATION = BASED_DIR + parser.get('happy', 'report_location')
LOG_FILENAME = BASED_DIR + parser.get('happy', 'log_filename')
YEAR = parser.get('happy', 'year')
logging.basicConfig(filename=LOG_FILENAME, level=logging.INFO)
DATE_LIST = pd.date_range(start=parser.get('happy', 'start_date'),end=parser.get('happy', 'end_date'))
totalMoney = 100000000
def simulateDaily():
investingMoney = 0
investingAmount = 300000000
for d in DATE_LIST:
portfolio = pd.read_csv(REPORT_LOCATION + "portfolio.csv", index_col="Stock")
dailyReports = pd.read_csv(REPORT_LOCATION + "reports.csv", index_col="ID")
dailyDf = []
# (dailyReports, dailyDf, portfolio, investingMoney, investingAmount) = analyzeAll(d, getCsvFiles(SELECTED_STOCK_LOCATION), dailyReports, dailyDf, portfolio, investingMoney, investingAmount);
# stockList = ['MBG.csv','HDC.csv','DRC.csv','TNG.csv','SBT.csv','APG.csv','CII.csv','AAA.csv','DXG.csv','PDR.csv','DPM.csv']
stockList = ['CII.csv']
