- refactored

git-svn-id: http://moon:8086/svn/projects/Stock@326 fda53097-d464-4ada-af97-ba876c37ca34

agent.py

@@ -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

@@ -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

@@ -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