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- avoid pandas for data arrays

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-  use more numpy consequently

git-svn-id: http://moon:8086/svn/projects/Stock@324 fda53097-d464-4ada-af97-ba876c37ca34
This commit is contained in:
jens
2019-12-22 22:38:58 +00:00
parent 6245198667
commit a34c4d1d2f
1 changed files with 122 additions and 83 deletions
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pandas_eval.py
+122 -83
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@@ -6,9 +6,7 @@ import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
import datetime as dt
import os
import matplotlib as mpl
mpl.rc('figure', max_open_warning = 0)
# Stock Investors Financial Math
# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
@@ -24,21 +22,21 @@ mpl.rc('figure', max_open_warning = 0)
# https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
key = '0UO7Z2MVZ2YSQSVE'
q_days = 20
thresh_macd = 1.5
show_range_days = 40
show_symbols = ['OHB.DE', 'ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS', 'UBSFF', 'DHER.DE', 'AIR']
#show_symbols = ['CSCO']
show_symbols = ['CSCO']
#show_symbols = ['OHB.DE']
#show_symbols = ['UBSFF']
#show_symbols = ['DHER.DE']
show_symbols = ['WDI.DE']
show_symbols = []
k_euro = 1 / 1.11
N_poly = 7
ema_alpha = 0.75
sma_days = 10
q_days = 10
fetch_on_outdated = True
mpl.rc('figure', max_open_warning = 0)
symbols = {
'WDI.DE' : {'name' : 'WireCard', 'currency' : '€'},
@@ -120,22 +118,25 @@ symbols = {
'I' : {'name': 'IntelSat', 'currency' : '$'}}
def exponential_moving_average(data, key, alpha=0.5, ic=None):
result = []
result = np.zeros_like(data[key])
if ic is None:
r = data[key][0]
else:
r = ic
n = 0
for v in data[key]:
r = alpha*r + (1-alpha)*v
result.append(r)
result[n] = r
n += 1
return np.array(result)
def moving_average(data, key, window_days, ic=0):
mem = [ic] * window_days
result = []
result = np.zeros_like(data[key])
k=0
cumsum = ic
n = 0
for v in data[key]:
cumsum += (v - mem[k])
mem[k] = v
@@ -143,18 +144,23 @@ def moving_average(data, key, window_days, ic=0):
if k >= window_days:
k=0
result.append(cumsum/window_days)
result[n] = cumsum/window_days
n += 1
return result
def moving_variance(data, key, window_days, ic=0):
mem = [ic] * window_days
result = []
result = np.zeros_like(data[key])
k=0
cumsum = ic
data_mean = data[key] - moving_average(data, key, window_days, ic)
n = 0
for v in data_mean:
v2 = v * v
try:
v2 = v * v
except:
print ('v', v)
cumsum += (v2 - mem[k])
mem[k] = v2
k += 1
@@ -162,55 +168,60 @@ def moving_variance(data, key, window_days, ic=0):
k=0
var = max(0, cumsum) / window_days
result.append(math.sqrt(var))
result[n] = math.sqrt(var)
n += 1
return result
def moving_max(data, key, window_days, ic=-1e9):
mem = [ic] * window_days
result = []
result = np.zeros_like(data[key])
k=0
n = 0
for v in data[key]:
mem[k] = v
k += 1
if k >= window_days:
k=0
_max = np.max(mem)
result.append(_max)
result[n] = np.max(mem)
n += 1
return result
def moving_min(data, key, window_days, ic=1e9):
mem = [ic] * window_days
result = []
result = np.zeros_like(data[key])
k=0
n = 0
for v in data[key]:
mem[k] = v
k += 1
if k >= window_days:
k=0
_min = np.min(mem)
result.append(_min)
result[n] = np.min(mem)
n += 1
return result
def normalize(data, key, ic=None):
result = []
result = np.zeros_like(data[key])
if ic is None:
prev = data[key][0]
else:
prev = ic
n = 0
for v in data[key]:
v_n = (v - prev)/prev
result.append(100*v_n)
result[n] = 100*v_n
n += 1
return result
def colsum(data, keys):
result = np.zeros(data.shape[0])
result = np.zeros(data['index'].shape)
for key in keys:
result += data[key]
@@ -223,39 +234,39 @@ def macd(data, key):
return ema_short - ema_long
def bollinger(data, window_days, f=2):
tp_ser = colsum(data, keys=['high', 'low', 'close']) / 3
tp_df = tp_ser.to_frame(name='tp')
stddev = np.array(moving_variance(tp_df, 'tp', window_days))
tp = colsum(data, keys=['high', 'low', 'close']) / 3
tp_dict = {'tp': tp}
stddev = np.array(moving_variance(tp_dict, 'tp', window_days))
mid = np.array(moving_average(tp_df, key='tp', window_days=window_days))
mid = np.array(moving_average(tp_dict, key='tp', window_days=window_days))
upper = mid + f * stddev
lower = mid - f * stddev
result = pd.DataFrame(upper, index=data.index, columns=['upper'])
result['lower'] = lower
result['mid'] = mid
result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}
return result
def agent_buy(name, data, thresh_max=-10, thresh_min=1):
buy_list = []
def agent_buy(name, data, cand_window, thresh_max=-10, thresh_min=1):
N = len(data['index'])
buy_list = np.array([None]*N)
cand = None
for n in range(0, len(data.index)):
for n in range(0, len(data['index'])):
vmax = data['Qmax'][n]
vmin = data['Qmin'][n]
trend = data['macd_fd'][n]
index = data.index[n]
if vmin <= thresh_min:
if vmax <= thresh_max:
cand = n
if trend >= 0:
print ("{}: Buy on {}".format(name, index))
buy_list.append({'name': name, 'date': index})
print ("{}: Buy on {} at {:0.2f}%".format(name, data['index'][cand], data['close_n'][cand]))
y = data['close_n'][n]
buy_list[n] = y
cand = None
if cand is not None:
if n - cand >= 5:
if n - cand <= cand_window:
if trend >= 0:
print("{}: Delayed buy on {}".format(name, data.index[cand]))
buy_list.append({'name': name, 'date': data.index[cand]})
print("{}: Delayed buy on {}".format(name, data['index'][n]))
y = data['close_n'][n]
buy_list[n] = y
cand = None
return buy_list
@@ -275,6 +286,9 @@ class Title(object):
if '$' in self.currency:
self.currency_corr = k_euro
self.data = {}
self.boll = {}
self.indicators = {}
def fetch(self):
filename = self.symbol.replace('.', '_') + '.h5'
@@ -304,79 +318,103 @@ class Title(object):
else:
hdf = pd.HDFStore(filename, 'r')
self.data_full = hdf[self.symbol] * self.currency_corr
data = hdf[self.symbol] * self.currency_corr
self.data['index'] = np.array(data.index)
self.data['close'] = np.array(data['close'])
self.data['high'] = np.array(data['high'])
self.data['low'] = np.array(data['low'])
hdf.close()
def __plot(self, d, keys):
dates = d.index
for key in keys:
data = d[key]
data.plot()
def hover(event):
if event.inaxes == self.ax:
x_idx = int(event.xdata)
self.ax.format_xdata = lambda x: dates[x_idx]
self.ax.format_ydata = lambda y: '€ {:.2f}'.format(data[x_idx])
self.fig.canvas.mpl_connect("motion_notify_event", hover)
def analyze(self):
N = len(self.data_full['close'])
self.data_full['ema'] = exponential_moving_average(self.data_full, key='close', alpha=ema_alpha)
self.data_full['macd'] = macd(self.data_full, key='close')
self.data_full['sma'] = moving_average(self.data_full, key='close', window_days=sma_days)
self.data_full['close_n'] = normalize(self.data_full, key='close')
self.data_full['min'] = moving_min(self.data_full, key='close_n', window_days=q_days)
self.data_full['max'] = moving_max(self.data_full, key='close_n', window_days=q_days)
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_full['Qmin'] = self.data_full['close_n'] - self.data_full['min']
self.data_full['Qmax'] = self.data_full['close_n'] - self.data_full['max']
self.boll_full = bollinger(self.data_full, window_days=30)
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_full['macd'].to_numpy()
y_r = self.data['macd']
spl = UnivariateSpline(x_r, y_r)
spl.set_smoothing_factor(0.5)
spl.set_smoothing_factor(0.25)
yf = spl(x_r)
yf_d = spl.derivative()(x_r)
self.data_full['macd_f'] = pd.DataFrame(np.transpose([yf]), index=self.data_full['macd'].index, columns=['macd_fitted'])
self.data_full['macd_fd'] = pd.DataFrame(np.transpose([yf_d]), index=self.data_full['macd'].index, columns=['macd_fitted_d'])
self.data['macd_f'] = np.transpose(yf)
self.data['macd_fd'] = np.transpose(yf_d)
_macd_f_curr = self.data_full['macd_f'][N-1]
_macd_fd_curr = self.data_full['macd_fd'][N-1]
self.data['Buy'] = agent_buy(self.symbol, self.data, cand_window=q_days)
self.data = self.data_full[len(self.data_full) - show_range_days:]
self.boll = self.boll_full[len(self.boll_full) - show_range_days:]
@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)
return agent_buy(self.symbol, self.data)
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 has_candidate(self, key, range_days):
N = len(self.data['index'])
start = max(0, N - range_days)
stop = N
sliced = Title.slice(self.data, start, stop)
result = np.count_nonzero(sliced[key] != None)
return result > 0
def show(self, figNum=1):
self.fig = plt.figure(figNum)
self.ax = plt.subplot(311)
self.__plot(self.data, ['min', 'max', 'close_n'])
self.__ind(self.data, ['Buy'])
plt.title('{} ({})'.format(self.name, self.symbol))
plt.legend()
plt.grid()
plt.subplot(312)
# self.boll['upper'].plot()
# self.boll['mid'].plot()
# self.boll['lower'].plot()
self.data['Qmin'].plot()
self.data['Qmax'].plot()
# self.__plot(self.boll, ['lower', 'mid', 'upper'])
self.__plot(self.data, ['Qmin', 'Qmax'])
plt.legend()
plt.grid()
plt.subplot(313)
self.data['macd_f'].plot()
self.data['macd_fd'].plot()
self.__plot(self.data, ['macd', 'macd_f', 'macd_fd'])
plt.legend()
plt.grid()
@@ -390,15 +428,16 @@ if len(show_symbols) > 0:
title = Title(symbol, symbols[symbol])
title.fetch()
title.analyze()
title.show(figNum)
title.show(figNum=figNum)
figNum += 1
else:
for symbol in symbols:
print('-----------------------------------------------')
title = Title(symbol, symbols[symbol])
title.fetch()
if len(title.analyze()) > 0:
title.show(figNum)
title.analyze()
if title.has_candidate(key='Buy', range_days=show_range_days):
title.show(figNum=figNum)
figNum += 1
plt.show()