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git-svn-id: http://moon:8086/svn/projects/Stock@327 fda53097-d464-4ada-af97-ba876c37ca34
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import pandas as pd
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import pandas_datareader.data as web
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from pandas_datareader._utils import RemoteDataError
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import matplotlib.pyplot as plt
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from scipy.interpolate import UnivariateSpline
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import datetime as dt
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import os
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import time
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import matplotlib as mpl
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import agent
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from functions import *
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# Stock Investors Financial Math
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# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
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# Alpha Vantage
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# https://www.alphavantage.co/documentation/
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# Pandas
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# https://pandas.pydata.org/pandas-docs/stable/index.html
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# Knowledge
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# https://www.investopedia.com/articles/technical/02/050602.asp
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# https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
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key = '0UO7Z2MVZ2YSQSVE'
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show_range_days = 5
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show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS', 'UBSFF', 'DHER.DE', 'AIR']
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show_symbols = ['CSCO']
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#show_symbols = ['OHB.DE']
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#show_symbols = ['UBSFF']
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show_symbols = ['DHER.DE']
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#show_symbols = ['WDI.DE']
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#show_symbols = ['EVT.DE']
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show_symbols = []
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k_euro = 1 / 1.11
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ema_alpha = 0.75
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sma_days = 10
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q_days = 10
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fetch_on_outdated = True
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mpl.rc('figure', max_open_warning = 0)
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symbols = {
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'RIG' : {'name' : 'Transocean Ltd.', 'currency' : '$'},
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'BBIO' : {'name' : 'BridgeBio Pharma, Inc.', 'currency' : '$'},
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'APA' : {'name' : 'Apache Corporation', 'currency' : '$'},
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'CBB-PB' : {'name' : 'Cincinnati Bell Inc.', 'currency' : '$'},
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'NMHLY' : {'name' : 'NMC Health Plc', 'currency' : '$'},
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'PINS' : {'name' : 'Pinterest', 'currency' : '$'},
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'TSLA' : {'name' : 'Tesla Inc.', 'currency' : '$'},
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'ACB' : {'name' : 'Aurora Cannabis Inc.', 'currency' : '$'},
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'ITCI' : {'name' : 'Intra-Cellular Therapies', 'currency' : '$'},
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'WDI.DE' : {'name' : 'WireCard', 'currency' : '€'},
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'AAPL' : {'name' : 'Apple', 'currency' : '$'},
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'XLNX' : {'name' : 'Xilinx', 'currency' : '$'},
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'QCOM' : {'name' : 'Qualcomm', 'currency' : '$'},
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'DPW.DE' : {'name' : 'Deutsche Post', 'currency' : '€'},
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'CSCO' : {'name' : 'Cisco', 'currency' : '$'},
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'AIR' : {'name' : 'Airbus', 'currency' : '$'},
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'BA' : {'name' : 'Boeing', 'currency' : '$'},
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'NVDA' : {'name' : 'Nvidia', 'currency' : '$'},
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'MSFT' : {'name' : 'Microsoft', 'currency' : '$'},
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'DIS' : {'name' : 'Disney', 'currency' : '$'},
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'NFLX' : {'name' : 'Netflix', 'currency' : '$'},
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'OHB.DE' : {'name' : 'OHB', 'currency' : '€'},
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'ERCA.DE' : {'name' : 'Ericsson', 'currency' : '€'},
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'VAR1.DE' : {'name' : 'Varta', 'currency' : '€'},
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'HD' : {'name' : 'Home Depot', 'currency' : '$'},
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'AMZN' : {'name' : 'Amazon', 'currency' : '$'},
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'GOOGL' : {'name' : 'Google', 'currency' : '$'},
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'FB2A.DE' : {'name': 'Facebook Inc.', 'currency' : '€'},
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'ZIL2.DE' : {'name': 'ElringKlinger', 'currency' : '€'},
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'SIS.DE' : {'name': 'First Sensor', 'currency' : '€'},
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'SIE.DE' : {'name': 'Siemens', 'currency' : '€'},
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'GFT.DE' : {'name': 'GFT Technologies', 'currency' : '€'},
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'AMD.DE' : {'name': 'Advanced Micro Devices', 'currency' : '€'},
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'CAP.DE' : {'name': 'Encavis', 'currency' : '€'},
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'ADBE' : {'name': 'Adobe', 'currency' : '$'},
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'PANW' : {'name': 'Palo Alto Networks', 'currency' : '$'},
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'AMAT' : {'name': 'Applied Materials', 'currency' : '$'},
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'RIB.DE' : {'name': 'RIB Software', 'currency' : '€'},
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'WAF.DE' : {'name': 'Siltronic', 'currency' : '€'},
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'EVT.DE' : {'name': 'Evotec', 'currency' : '€'},
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'VOW.DE' : {'name': 'Volkswagen', 'currency' : '€'},
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'BMW.DE' : {'name': 'BMW', 'currency' : '€'},
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'NSU.DE' : {'name': 'Audi', 'currency' : '€'},
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'DAI.DE' : {'name': 'Daimler', 'currency' : '€'},
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'SHA.DE' : {'name': 'Schaeffler', 'currency' : '€'},
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'CON.DE' : {'name': 'Continental', 'currency' : '€'},
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'DHER.DE' : {'name': 'Delivery Hero', 'currency' : '€'},
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'BSL.DE' : {'name': 'Basler', 'currency' : '€'},
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'D6H.DE' : {'name': 'DATAGROUP', 'currency' : '€'},
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'TC1.DE' : {'name': 'Tele Columbus', 'currency' : '€'},
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'VODI.DE' : {'name': 'Vodaphone Group', 'currency' : '€'},
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'ATVI' : {'name': 'Activision Blizzard', 'currency' : '$'},
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'GME' : {'name': 'GameStop', 'currency' : '$'},
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'NTO.F' : {'name': 'Nintendo', 'currency' : '$'},
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'UBSFF' : {'name': 'UBI Soft', 'currency' : '$'},
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'NXPRF' : {'name': 'Nexans', 'currency' : '$'},
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# 'IMPUF' : {'name': 'Impala Platinum Holdings', 'currency' : '$'},
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'NMPNF' : {'name': 'Northam Platinum', 'currency' : '$'},
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'ALSMY' : {'name': 'Alstom', 'currency' : '$'},
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'ARRD.F' : {'name': 'Arcelor Mittal', 'currency' : '$'},
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'PHG' : {'name': 'Philips', 'currency' : '$'},
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'KEYS' : {'name': 'KeySight', 'currency' : '$'},
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'ORCL' : {'name': 'Oracle', 'currency' : '$'},
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'UBER' : {'name': 'Uber', 'currency' : '$'},
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'VMW' : {'name': 'VMWare', 'currency' : '$'},
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'AVGO' : {'name' : 'Avago', 'currency' : '$'},
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'CY' : {'name': 'Cypress Semiconductor', 'currency' : '$'},
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'QABSY' : {'name': 'Qanta Airways', 'currency' : '$'},
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'BLDP' : {'name': 'Ballard Power', 'currency' : '$'},
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'D7G.F' : {'name': 'Nel ASA', 'currency' : '$'},
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'ITMPF' : {'name': 'ITM Power', 'currency' : '$'},
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'PLUG' : {'name': 'PlugPower', 'currency' : '$'},
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'PCELF' : {'name': 'PowerCell', 'currency' : '$'},
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'SU.PA' : {'name': 'Schneider Electric', 'currency' : '$'},
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# 'SON1.DE' : {'name': '', 'currency' : '€'},
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'STM.DE' : {'name': 'STM Micro', 'currency' : '€'},
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'BC8.DE' : {'name': 'Bechtle', 'currency' : '€'},
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'MOR.DE' : {'name' : 'Morphosys', 'currency' : '€'},
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'BAYN.DE' : {'name' : 'Bayer', 'currency' : '€'},
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'BEI.DE' : {'name' : 'Beiersdorf', 'currency' : '€'},
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'EUZ.DE' : {'name': 'Eckert & Ziegler', 'currency' : '€'},
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'SLAB' : {'name': 'Silicon Laboratories', 'currency' : '$'},
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'SPLK' : {'name' : 'Splunk', 'currency' : '$'},
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'IAG' : {'name' : 'IAG', 'currency' : '$'},
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'INTC' : {'name' : 'Intel Corporation', 'currency' : '$'},
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'I' : {'name': 'IntelSat', 'currency' : '$'}}
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class Title(object):
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def __init__(self, symbol, params):
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self.symbol = symbol
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self.params = params
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try:
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self.name = params['name']
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except:
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self.name = symbol
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self.ax = None
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self.fig = None
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self.currency = params['currency']
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self.currency_corr = 1
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if '$' in self.currency:
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self.currency_corr = k_euro
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self.data = {}
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self.boll = {}
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self.indicators = {}
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def fetch(self):
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filename = self.symbol.replace('.', '_') + '.h5'
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fetch = False
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today = dt.date.today()
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start = dt.datetime(today.year, 1, 1)
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end = today
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try:
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hdf = pd.HDFStore(filename, 'r')
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hdf.close()
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except:
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fetch = True
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try:
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lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
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if lastmodified != today:
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fetch = fetch_on_outdated
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except:
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fetch = True
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if fetch:
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# Get stock price via data reader
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while(True):
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try:
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print("Fetching \"{}\"".format(self.symbol))
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data = web.DataReader(self.symbol, "av-daily", start, end, api_key=key)
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break
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except RemoteDataError:
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for timeout in reversed(range(0, 60)):
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print ("Try again in {} s".format(timeout))
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time.sleep(1)
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hdf = pd.HDFStore(filename)
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hdf[self.symbol] = data
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else:
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hdf = pd.HDFStore(filename, 'r')
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data = hdf[self.symbol] * self.currency_corr
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N = len(data.index)
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self.data['index'] = np.array(data.index[0:N])
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self.data['close'] = np.array(data['close'][0:N])
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self.data['high'] = np.array(data['high'][0:N])
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self.data['low'] = np.array(data['low'][0:N])
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hdf.close()
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def get_latest(self, key):
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N = len(self.data['index'])
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return self.data[key][N-1]
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def statistics(self):
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N = len(self.data['index'])
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self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=ema_alpha)
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self.data['sma'] = moving_average(self.data, key='close', window_days=sma_days)
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self.data['close_n'] = normalize(self.data, key='close')
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self.data['macd'] = macd(self.data, key='close_n')
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self.data['min'] = moving_min(self.data, key='close_n', window_days=q_days)
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self.data['max'] = moving_max(self.data, key='close_n', window_days=q_days)
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self.data['Qmin'] = self.data['close_n'] - self.data['min']
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self.data['Qmax'] = self.data['close_n'] - self.data['max']
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self.boll = bollinger(self.data, window_days=30)
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x_r = np.linspace(0, N, N)
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y_r = self.data['macd']
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spl = UnivariateSpline(x_r, y_r)
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spl.set_smoothing_factor(0.25)
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spl_d = spl.derivative()
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spl_dd = spl_d.derivative()
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yf = spl(x_r)
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yf_d = spl_d(x_r)
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yf_dd = spl_dd(x_r)
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self.data['macd_f'] = np.transpose(yf)
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self.data['macd_fd'] = np.transpose(yf_d)
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self.data['macd_fdd'] = np.transpose(yf_dd)
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def analyze(self, buy_callback, range_days):
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return agent.buy_BTFD(self.symbol, self.data, marker_key='close_n', cand_window=5, range_days=range_days, buy_callback=buy_callback)
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@staticmethod
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def has_candidate(data, key):
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result = np.count_nonzero(data[key] != None)
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return result > 0
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@staticmethod
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def slice(data, start, stop):
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result = {}
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for key in iter(data):
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result[key] = data[key][start:stop]
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return result
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def __plot(self, data, keys):
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N = len(data['index'])
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start = max(0, N - show_range_days)
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stop = N
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xr = list(range(start, stop))
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sliced = Title.slice(data, start, stop)
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for key in keys:
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plt.plot(xr, sliced[key], label=key)
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def hover(event):
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if event.inaxes == self.ax:
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x_idx = min(N-1, int(event.xdata))
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self.ax.format_xdata = lambda x: self.data['index'][x_idx]
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self.ax.format_ydata = lambda y: '{:.2f}%'.format(self.data['close_n'][x_idx])
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self.fig.canvas.mpl_connect("motion_notify_event", hover)
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def __ind(self, data, keys):
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N = len(data['index'])
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start = max(0, N - show_range_days)
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stop = N
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xr = list(range(start, stop))
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sliced = Title.slice(data, start, stop)
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for key in keys:
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plt.plot(xr, sliced[key], 'go', label=key)
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def show(self, indicators, figNum=1):
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num_subplots = 4
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self.fig = plt.figure(figNum)
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self.ax = plt.subplot(100*num_subplots + 10 + 1)
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self.__plot(self.data, ['min', 'max', 'close_n'])
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self.__ind(indicators, ['BTFD'])
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plt.title('{} ({})'.format(self.name, self.symbol))
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plt.legend()
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plt.grid()
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plt.subplot(100*num_subplots + 10 + 2)
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# self.__plot(self.boll, ['lower', 'mid', 'upper'])
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self.__plot(self.data, ['Qmin', 'Qmax'])
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plt.legend()
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plt.grid()
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plt.subplot(100*num_subplots + 10 + 3)
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self.__plot(self.data, ['macd', 'macd_f'])
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plt.legend()
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plt.grid()
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plt.subplot(100*num_subplots + 10 + 4)
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self.__plot(self.data, ['macd_fd', 'macd_fdd'])
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plt.legend()
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plt.grid()
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buy_list = {}
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def buy_callback(data):
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name = data['name']
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item = data['item']
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if name not in buy_list:
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buy_list[name] = {'items': [item]}
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else:
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buy_list[name]['items'].append(item)
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print(data)
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figNum = 1
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if len(show_symbols) > 0:
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for symbol in show_symbols:
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if symbol in symbols:
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title = Title(symbol, symbols[symbol])
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title.fetch()
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title.statistics()
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ind = title.analyze(buy_callback, range_days=show_range_days)
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title.show(ind, figNum=figNum)
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figNum += 1
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else:
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for symbol in symbols:
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title = Title(symbol, symbols[symbol])
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title.fetch()
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title.statistics()
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ind = title.analyze(buy_callback, range_days=show_range_days)
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if Title.has_candidate(ind, 'BTFD'):
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print('-----------------------------------------------')
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title.show(ind, figNum=figNum)
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figNum += 1
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gain_accum = 0
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num_stocks = 0
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for symbol in buy_list:
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items = buy_list[symbol]['items']
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title = Title(symbol, symbols[symbol])
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title.fetch()
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title.statistics()
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gain = title.get_latest('close_n') - items[0]['value']
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gain_accum += gain
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num_stocks += 1
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print ('{}: Gain = {} %'.format(symbol, gain))
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if num_stocks > 0:
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print ('Gain total = {} %'.format(gain_accum/num_stocks))
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plt.show()
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Reference in New Issue
Block a user