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- refactored

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git-svn-id: http://moon:8086/svn/projects/Stock@328 fda53097-d464-4ada-af97-ba876c37ca34
This commit is contained in:
jens
2019-12-25 19:18:55 +00:00
parent 3d600d9ecf
commit 8889b6c77c
2 changed files with 200 additions and 195 deletions
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robot.py
+18 -195
View File
@@ -1,15 +1,3 @@
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader._utils import RemoteDataError
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
import datetime as dt
import os
import time
import matplotlib as mpl
import agent
from functions import *
# Stock Investors Financial Math
# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
@@ -23,8 +11,19 @@ from functions import *
# https://www.investopedia.com/articles/technical/02/050602.asp
# https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
import matplotlib.pyplot as plt
from stock import Stock
key = '0UO7Z2MVZ2YSQSVE'
show_range_days = 5
params = {
'show_range_days' : 5,
'k_euro' : 1 / 1.11,
'ema_alpha' : 0.75,
'sma_days' : 10,
'q_days' : 10,
'fetch_on_outdated' : True
}
show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS', 'UBSFF', 'DHER.DE', 'AIR']
show_symbols = ['CSCO']
#show_symbols = ['OHB.DE']
@@ -33,13 +32,6 @@ show_symbols = ['DHER.DE']
#show_symbols = ['WDI.DE']
#show_symbols = ['EVT.DE']
show_symbols = []
k_euro = 1 / 1.11
ema_alpha = 0.75
sma_days = 10
q_days = 10
fetch_on_outdated = True
mpl.rc('figure', max_open_warning = 0)
symbols = {
@@ -130,175 +122,6 @@ symbols = {
'INTC' : {'name' : 'Intel Corporation', 'currency' : '$'},
'I' : {'name': 'IntelSat', 'currency' : '$'}}
class Title(object):
def __init__(self, symbol, params):
self.symbol = symbol
self.params = params
try:
self.name = params['name']
except:
self.name = symbol
self.ax = None
self.fig = None
self.currency = params['currency']
self.currency_corr = 1
if '$' in self.currency:
self.currency_corr = k_euro
self.data = {}
self.boll = {}
self.indicators = {}
def fetch(self):
filename = self.symbol.replace('.', '_') + '.h5'
fetch = False
today = dt.date.today()
start = dt.datetime(today.year, 1, 1)
end = today
try:
hdf = pd.HDFStore(filename, 'r')
hdf.close()
except:
fetch = True
try:
lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
if lastmodified != today:
fetch = fetch_on_outdated
except:
fetch = True
if fetch:
# Get stock price via data reader
while(True):
try:
print("Fetching \"{}\"".format(self.symbol))
data = web.DataReader(self.symbol, "av-daily", start, end, api_key=key)
break
except RemoteDataError:
for timeout in reversed(range(0, 60)):
print ("Try again in {} s".format(timeout))
time.sleep(1)
hdf = pd.HDFStore(filename)
hdf[self.symbol] = data
else:
hdf = pd.HDFStore(filename, 'r')
data = hdf[self.symbol] * self.currency_corr
N = len(data.index)
self.data['index'] = np.array(data.index[0:N])
self.data['close'] = np.array(data['close'][0:N])
self.data['high'] = np.array(data['high'][0:N])
self.data['low'] = np.array(data['low'][0:N])
hdf.close()
def get_latest(self, key):
N = len(self.data['index'])
return self.data[key][N-1]
def statistics(self):
N = len(self.data['index'])
self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=ema_alpha)
self.data['sma'] = moving_average(self.data, key='close', window_days=sma_days)
self.data['close_n'] = normalize(self.data, key='close')
self.data['macd'] = macd(self.data, key='close_n')
self.data['min'] = moving_min(self.data, key='close_n', window_days=q_days)
self.data['max'] = moving_max(self.data, key='close_n', window_days=q_days)
self.data['Qmin'] = self.data['close_n'] - self.data['min']
self.data['Qmax'] = self.data['close_n'] - self.data['max']
self.boll = bollinger(self.data, window_days=30)
x_r = np.linspace(0, N, N)
y_r = self.data['macd']
spl = UnivariateSpline(x_r, y_r)
spl.set_smoothing_factor(0.25)
spl_d = spl.derivative()
spl_dd = spl_d.derivative()
yf = spl(x_r)
yf_d = spl_d(x_r)
yf_dd = spl_dd(x_r)
self.data['macd_f'] = np.transpose(yf)
self.data['macd_fd'] = np.transpose(yf_d)
self.data['macd_fdd'] = np.transpose(yf_dd)
def analyze(self, buy_callback, range_days):
return agent.buy_BTFD(self.symbol, self.data, marker_key='close_n', cand_window=5, range_days=range_days, buy_callback=buy_callback)
@staticmethod
def has_candidate(data, key):
result = np.count_nonzero(data[key] != None)
return result > 0
@staticmethod
def slice(data, start, stop):
result = {}
for key in iter(data):
result[key] = data[key][start:stop]
return result
def __plot(self, data, keys):
N = len(data['index'])
start = max(0, N - show_range_days)
stop = N
xr = list(range(start, stop))
sliced = Title.slice(data, start, stop)
for key in keys:
plt.plot(xr, sliced[key], label=key)
def hover(event):
if event.inaxes == self.ax:
x_idx = min(N-1, int(event.xdata))
self.ax.format_xdata = lambda x: self.data['index'][x_idx]
self.ax.format_ydata = lambda y: '{:.2f}%'.format(self.data['close_n'][x_idx])
self.fig.canvas.mpl_connect("motion_notify_event", hover)
def __ind(self, data, keys):
N = len(data['index'])
start = max(0, N - show_range_days)
stop = N
xr = list(range(start, stop))
sliced = Title.slice(data, start, stop)
for key in keys:
plt.plot(xr, sliced[key], 'go', label=key)
def show(self, indicators, figNum=1):
num_subplots = 4
self.fig = plt.figure(figNum)
self.ax = plt.subplot(100*num_subplots + 10 + 1)
self.__plot(self.data, ['min', 'max', 'close_n'])
self.__ind(indicators, ['BTFD'])
plt.title('{} ({})'.format(self.name, self.symbol))
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 2)
# self.__plot(self.boll, ['lower', 'mid', 'upper'])
self.__plot(self.data, ['Qmin', 'Qmax'])
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 3)
self.__plot(self.data, ['macd', 'macd_f'])
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 4)
self.__plot(self.data, ['macd_fd', 'macd_fdd'])
plt.legend()
plt.grid()
buy_list = {}
def buy_callback(data):
name = data['name']
@@ -314,19 +137,19 @@ figNum = 1
if len(show_symbols) > 0:
for symbol in show_symbols:
if symbol in symbols:
title = Title(symbol, symbols[symbol])
title = Stock(symbol, symbols[symbol])
title.fetch()
title.statistics()
ind = title.analyze(buy_callback, range_days=show_range_days)
ind = title.analyze(buy_callback, range_days=params['show_range_days'])
title.show(ind, figNum=figNum)
figNum += 1
else:
for symbol in symbols:
title = Title(symbol, symbols[symbol])
title = Stock(params, symbol, symbols[symbol])
title.fetch()
title.statistics()
ind = title.analyze(buy_callback, range_days=show_range_days)
if Title.has_candidate(ind, 'BTFD'):
ind = title.analyze(buy_callback, range_days=params['show_range_days'])
if Stock.has_candidate(ind, 'BTFD'):
print('-----------------------------------------------')
title.show(ind, figNum=figNum)
figNum += 1
@@ -335,7 +158,7 @@ gain_accum = 0
num_stocks = 0
for symbol in buy_list:
items = buy_list[symbol]['items']
title = Title(symbol, symbols[symbol])
title = Stock(params, symbol, symbols[symbol])
title.fetch()
title.statistics()
gain = title.get_latest('close_n') - items[0]['value']
stock.py
+182
View File
@@ -0,0 +1,182 @@
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader._utils import RemoteDataError
from scipy.interpolate import UnivariateSpline
import datetime as dt
import os
import time
import agent
from functions import *
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rc('figure', max_open_warning = 0)
class Stock(object):
def __init__(self, params2, symbol, params):
self.params = params
self.symbol = symbol
self.params2 = params2
try:
self.name = params['name']
except:
self.name = symbol
self.ax = None
self.fig = None
self.currency = params['currency']
self.currency_corr = 1
if '$' in self.currency:
self.currency_corr = self.params2['k_euro']
self.data = {}
self.boll = {}
self.indicators = {}
def fetch(self):
filename = self.symbol.replace('.', '_') + '.h5'
fetch = False
today = dt.date.today()
start = dt.datetime(today.year, 1, 1)
end = today
try:
hdf = pd.HDFStore(filename, 'r')
hdf.close()
except:
fetch = True
try:
lastmodified = dt.datetime.fromtimestamp(os.stat(filename).st_mtime).date()
if lastmodified != today:
fetch = fetch_on_outdated
except:
fetch = True
if fetch:
# Get stock price via data reader
while(True):
try:
print("Fetching \"{}\"".format(self.symbol))
data = web.DataReader(self.symbol, "av-daily", start, end, api_key=key)
break
except RemoteDataError:
for timeout in reversed(range(0, 60)):
print ("Try again in {} s".format(timeout))
time.sleep(1)
hdf = pd.HDFStore(filename)
hdf[self.symbol] = data
else:
hdf = pd.HDFStore(filename, 'r')
data = hdf[self.symbol] * self.currency_corr
N = len(data.index)
self.data['index'] = np.array(data.index[0:N])
self.data['close'] = np.array(data['close'][0:N])
self.data['high'] = np.array(data['high'][0:N])
self.data['low'] = np.array(data['low'][0:N])
hdf.close()
def get_latest(self, key):
N = len(self.data['index'])
return self.data[key][N-1]
def statistics(self):
N = len(self.data['index'])
self.data['ema'] = exponential_moving_average(self.data, key='close', alpha=self.params2['ema_alpha'])
self.data['sma'] = moving_average(self.data, key='close', window_days=self.params2['sma_days'])
self.data['close_n'] = normalize(self.data, key='close')
self.data['macd'] = macd(self.data, key='close_n')
self.data['min'] = moving_min(self.data, key='close_n', window_days=self.params2['q_days'])
self.data['max'] = moving_max(self.data, key='close_n', window_days=self.params2['q_days'])
self.data['Qmin'] = self.data['close_n'] - self.data['min']
self.data['Qmax'] = self.data['close_n'] - self.data['max']
self.boll = bollinger(self.data, window_days=30)
x_r = np.linspace(0, N, N)
y_r = self.data['macd']
spl = UnivariateSpline(x_r, y_r)
spl.set_smoothing_factor(0.25)
spl_d = spl.derivative()
spl_dd = spl_d.derivative()
yf = spl(x_r)
yf_d = spl_d(x_r)
yf_dd = spl_dd(x_r)
self.data['macd_f'] = np.transpose(yf)
self.data['macd_fd'] = np.transpose(yf_d)
self.data['macd_fdd'] = np.transpose(yf_dd)
def analyze(self, buy_callback, range_days):
return agent.buy_BTFD(self.symbol, self.data, marker_key='close_n', cand_window=5, range_days=range_days, buy_callback=buy_callback)
@staticmethod
def has_candidate(data, key):
result = np.count_nonzero(data[key] != None)
return result > 0
@staticmethod
def slice(data, start, stop):
result = {}
for key in iter(data):
result[key] = data[key][start:stop]
return result
def __plot(self, data, keys):
N = len(data['index'])
start = max(0, N - self.params2['show_range_days'])
stop = N
xr = list(range(start, stop))
sliced = Stock.slice(data, start, stop)
for key in keys:
plt.plot(xr, sliced[key], label=key)
def hover(event):
if event.inaxes == self.ax:
x_idx = min(N-1, int(event.xdata))
self.ax.format_xdata = lambda x: self.data['index'][x_idx]
self.ax.format_ydata = lambda y: '{:.2f}%'.format(self.data['close_n'][x_idx])
self.fig.canvas.mpl_connect("motion_notify_event", hover)
def __ind(self, data, keys):
N = len(data['index'])
start = max(0, N - self.params2['show_range_days'])
stop = N
xr = list(range(start, stop))
sliced = Stock.slice(data, start, stop)
for key in keys:
plt.plot(xr, sliced[key], 'go', label=key)
def show(self, indicators, figNum=1):
num_subplots = 4
self.fig = plt.figure(figNum)
self.ax = plt.subplot(100*num_subplots + 10 + 1)
self.__plot(self.data, ['min', 'max', 'close_n'])
self.__ind(indicators, ['BTFD'])
plt.title('{} ({})'.format(self.name, self.symbol))
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 2)
# self.__plot(self.boll, ['lower', 'mid', 'upper'])
self.__plot(self.data, ['Qmin', 'Qmax'])
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 3)
self.__plot(self.data, ['macd', 'macd_f'])
plt.legend()
plt.grid()
plt.subplot(100*num_subplots + 10 + 4)
self.__plot(self.data, ['macd_fd', 'macd_fdd'])
plt.legend()
plt.grid()