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- refactored

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git-svn-id: http://moon:8086/svn/projects/Stock@326 fda53097-d464-4ada-af97-ba876c37ca34
This commit is contained in:
jens
2019-12-25 19:02:31 +00:00
parent 5da384ec18
commit 947a843128
3 changed files with 187 additions and 179 deletions
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agent.py
+42
View File
@@ -0,0 +1,42 @@
import numpy as np
def buy_BTFD(name, data, range_days, cand_window, marker_key='close_n', thresh_max=-10, thresh_min=1, buy_callback=None):
N = len(data['index'])
key = 'BTFD'
buy_list = {'index' : np.array([None]*N), key : np.array([None]*N)}
cand = None
do_buy = False
for n in range(N-range_days, N):
vmax = data['Qmax'][n]
vmin = data['Qmin'][n]
trend = data['macd_fdd'][n]
index = data['index'][n]
value = data[marker_key][n]
if vmin <= thresh_min:
if vmax <= thresh_max:
cand = n
if trend >= 0:
do_buy = True
print ("{}: Buy on {} at {:0.2f}".format(name, index, value))
cand = None
if cand is not None:
if n - cand <= cand_window:
if trend >= 0:
do_buy = True
former = data[marker_key][cand]
print("{}: Delayed buy on {} at {:0.2f} ({:0.2f})".format(name, index, value, former-value))
cand = None
else:
cand = None
if do_buy:
do_buy = False
buy_list['index'][n] = index
buy_list[key][n] = value
if buy_callback is not None:
buy_callback({'name': name, 'item': {'value': value, 'date': index}})
return buy_list
functions.py
+137
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@@ -0,0 +1,137 @@
import numpy as np
import math
def exponential_moving_average(data, key, alpha=0.5, ic=None):
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[n] = r
n += 1
return np.array(result)
def moving_average(data, key, window_days, ic=0):
mem = [ic] * window_days
result = np.zeros_like(data[key])
k=0
cumsum = ic
n = 0
for v in data[key]:
cumsum += (v - mem[k])
mem[k] = v
k += 1
if k >= window_days:
k=0
result[n] = cumsum/window_days
n += 1
return result
def moving_variance(data, key, window_days, ic=0):
mem = [ic] * window_days
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:
try:
v2 = v * v
except:
print ('v', v)
cumsum += (v2 - mem[k])
mem[k] = v2
k += 1
if k >= window_days:
k=0
var = max(0, cumsum) / window_days
result[n] = math.sqrt(var)
n += 1
return result
def moving_max(data, key, window_days, ic=-1e9):
mem = [ic] * window_days
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
result[n] = np.max(mem)
n += 1
return result
def moving_min(data, key, window_days, ic=1e9):
mem = [ic] * window_days
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
result[n] = np.min(mem)
n += 1
return result
def normalize(data, key, ic=None):
result = np.zeros_like(data[key])
cumsum = 0
if ic is None:
prev = data[key][0]
else:
prev = ic
n = 0
for v in data[key]:
if prev == 0:
print (prev)
cumsum += 100*(v - prev)/(prev)
result[n] = cumsum
if v > 0:
prev = v
n += 1
return result
def colsum(data, keys):
result = np.zeros(data['index'].shape)
for key in keys:
result += data[key]
return result
def macd(data, key):
ema_short = exponential_moving_average(data, key, alpha=0.85)
ema_long = exponential_moving_average(data, key, alpha=0.925)
return ema_short - ema_long
def bollinger(data, window_days, f=2):
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_dict, key='tp', window_days=window_days))
upper = mid + f * stddev
lower = mid - f * stddev
result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}
return result
pandas_eval.py
+8 -179
View File
@@ -1,8 +1,6 @@
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader._utils import RemoteDataError
import numpy as np
import math
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
import datetime as dt
@@ -10,6 +8,8 @@ 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
@@ -130,177 +130,6 @@ symbols = {
'INTC' : {'name' : 'Intel Corporation', 'currency' : '$'},
'I' : {'name': 'IntelSat', 'currency' : '$'}}
def exponential_moving_average(data, key, alpha=0.5, ic=None):
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[n] = r
n += 1
return np.array(result)
def moving_average(data, key, window_days, ic=0):
mem = [ic] * window_days
result = np.zeros_like(data[key])
k=0
cumsum = ic
n = 0
for v in data[key]:
cumsum += (v - mem[k])
mem[k] = v
k += 1
if k >= window_days:
k=0
result[n] = cumsum/window_days
n += 1
return result
def moving_variance(data, key, window_days, ic=0):
mem = [ic] * window_days
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:
try:
v2 = v * v
except:
print ('v', v)
cumsum += (v2 - mem[k])
mem[k] = v2
k += 1
if k >= window_days:
k=0
var = max(0, cumsum) / window_days
result[n] = math.sqrt(var)
n += 1
return result
def moving_max(data, key, window_days, ic=-1e9):
mem = [ic] * window_days
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
result[n] = np.max(mem)
n += 1
return result
def moving_min(data, key, window_days, ic=1e9):
mem = [ic] * window_days
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
result[n] = np.min(mem)
n += 1
return result
def normalize(data, key, ic=None):
result = np.zeros_like(data[key])
cumsum = 0
if ic is None:
prev = data[key][0]
else:
prev = ic
n = 0
for v in data[key]:
if prev == 0:
print (prev)
cumsum += 100*(v - prev)/(prev)
result[n] = cumsum
if v > 0:
prev = v
n += 1
return result
def colsum(data, keys):
result = np.zeros(data['index'].shape)
for key in keys:
result += data[key]
return result
def macd(data, key):
ema_short = exponential_moving_average(data, key, alpha=0.85)
ema_long = exponential_moving_average(data, key, alpha=0.925)
return ema_short - ema_long
def bollinger(data, window_days, f=2):
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_dict, key='tp', window_days=window_days))
upper = mid + f * stddev
lower = mid - f * stddev
result = {'index' : data['index'], 'upper' : upper, 'mid' : mid, 'lower' : lower}
return result
def agent_buy(name, data, range_days, cand_window, marker_key='close_n', thresh_max=-10, thresh_min=1, buy_callback=None):
N = len(data['index'])
buy_list = {'index' : np.array([None]*N), 'Buy_dip' : np.array([None]*N)}
cand = None
do_buy = False
for n in range(N-range_days, N):
vmax = data['Qmax'][n]
vmin = data['Qmin'][n]
trend = data['macd_fdd'][n]
index = data['index'][n]
value = data[marker_key][n]
if vmin <= thresh_min:
if vmax <= thresh_max:
cand = n
if trend >= 0:
do_buy = True
print ("{}: Buy on {} at {:0.2f}".format(name, index, value))
cand = None
if cand is not None:
if n - cand <= cand_window:
if trend >= 0:
do_buy = True
former = data[marker_key][cand]
print("{}: Delayed buy on {} at {:0.2f} ({:0.2f})".format(name, index, value, former-value))
cand = None
else:
cand = None
if do_buy:
do_buy = False
buy_list['index'][n] = index
buy_list['Buy_dip'][n] = value
if buy_callback is not None:
buy_callback({'name': name, 'item': {'value': value, 'date': index}})
return buy_list
class Title(object):
def __init__(self, symbol, params):
self.symbol = symbol
@@ -398,7 +227,7 @@ class Title(object):
self.data['macd_fdd'] = np.transpose(yf_dd)
def analyze(self, buy_callback, range_days):
return agent_buy(self.symbol, self.data, marker_key='close_n', cand_window=5, range_days=range_days, buy_callback=buy_callback)
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):
@@ -447,24 +276,24 @@ class Title(object):
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, ['Buy_dip'])
self.__ind(indicators, ['BTFD'])
plt.title('{} ({})'.format(self.name, self.symbol))
plt.legend()
plt.grid()
self.ax = plt.subplot(100*num_subplots + 10 + 2)
plt.subplot(100*num_subplots + 10 + 2)
# self.__plot(self.boll, ['lower', 'mid', 'upper'])
self.__plot(self.data, ['Qmin', 'Qmax'])
plt.legend()
plt.grid()
self.ax = plt.subplot(100*num_subplots + 10 + 3)
plt.subplot(100*num_subplots + 10 + 3)
self.__plot(self.data, ['macd', 'macd_f'])
plt.legend()
plt.grid()
self.ax = plt.subplot(100*num_subplots + 10 + 4)
plt.subplot(100*num_subplots + 10 + 4)
self.__plot(self.data, ['macd_fd', 'macd_fdd'])
plt.legend()
plt.grid()
@@ -497,7 +326,7 @@ else:
title.fetch()
title.statistics()
ind = title.analyze(buy_callback, range_days=show_range_days)
if Title.has_candidate(ind, 'Buy_dip'):
if Title.has_candidate(ind, 'BTFD'):
print('-----------------------------------------------')
title.show(ind, figNum=figNum)
figNum += 1