- refactored
- cleaned up - added buy agent git-svn-id: http://moon:8086/svn/projects/Stock@322 fda53097-d464-4ada-af97-ba876c37ca34
This commit is contained in:
+164
-79
@@ -7,6 +7,9 @@ from scipy.interpolate import UnivariateSpline
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import datetime as dt
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import os
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import matplotlib as mpl
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mpl.rc('figure', max_open_warning = 0)
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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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@@ -21,14 +24,21 @@ import os
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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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q_days = 20
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thresh_macd = 1.5
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show_range_days = 20
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show_symbols = ['ITMPF', 'PLUG', 'MOR.DE', 'CSCO', 'ERCA.DE', 'AVGO', 'DIS']
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#show_symbols = []
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show_range_days = 40
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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 = []
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k_euro = 1 / 1.11
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N_poly = 7
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ema_alpha = 0.75
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sma_days = 10
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fetch_on_outdated = True
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symbols = {
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'WDI.DE' : {'name' : 'WireCard', 'currency' : '€'},
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@@ -52,64 +62,64 @@ symbols = {
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'FB2A.DE' : {'currency' : '€'},
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'ZIL2.DE' : {'currency' : '€'},
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'SIS.DE' : {'currency' : '€'},
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'SIE.DE' : {'currency' : '€'},
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'SIE.DE' : {'name': 'Siemens', 'currency' : '€'},
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'GFT.DE' : {'currency' : '€'},
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'AMD.DE' : {'currency' : '€'},
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'CAP.DE' : {'currency' : '€'},
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'ADBE' : {'currency' : '$'},
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'PANW' : {'currency' : '$'},
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'AMAT' : {'currency' : '$'},
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'RIB.DE' : {'currency' : '€'},
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'WAF.DE' : {'currency' : '€'},
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'EVT.DE' : {'currency' : '€'},
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'VOW.DE' : {'currency' : '€'},
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'BMW.DE' : {'currency' : '€'},
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'NSU.DE' : {'currency' : '€'},
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'DAI.DE' : {'currency' : '€'},
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'SHA.DE' : {'currency' : '€'},
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'CON.DE' : {'currency' : '€'},
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'AMD.DE' : {'name': 'Advanced Micro Devices', 'currency' : '€'},
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'CAP.DE' : {'name': 'Encavis', 'currency' : '€'},
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'ADBE' : {'name': 'Adobe', 'currency' : '$'},
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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' : {'currency' : '€'},
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'BSL.DE' : {'name': 'Basler', 'currency' : '€'},
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'D6H.DE' : {'currency' : '€'},
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'TC1.DE' : {'currency' : '€'},
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'VODI.DE' : {'currency' : '€'},
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'ATVI' : {'currency' : '$'},
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'GME' : {'currency' : '$'},
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'NTO.F' : {'currency' : '$'},
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'UBSFF' : {'currency' : '$'},
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'NXPRF' : {'currency' : '$'},
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'IMPUF' : {'currency' : '$'},
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'NMPNF' : {'currency' : '$'},
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'ALSMY' : {'currency' : '$'},
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'ARRD.F' : {'currency' : '$'},
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'PHG' : {'currency' : '$'},
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'KEYS' : {'currency' : '$'},
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'ORCL' : {'currency' : '$'},
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'UBER' : {'currency' : '$'},
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'VMW' : {'currency' : '$'},
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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' : '$'},
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'CY' : {'currency' : '$'},
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'QABSY' : {'currency' : '$'},
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'BLDP' : {'currency' : '$'},
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'D7G.F' : {'currency' : '$'},
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'ITMPF' : {'currency' : '$'},
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'PLUG' : {'currency' : '$'},
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'PCELF' : {'currency' : '$'},
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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' : '$'},
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'SU.PA' : {'currency' : '$'},
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# 'SON1.DE' : {'currency' : '€'},
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'STM.DE' : {'currency' : '€'},
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'BC8.DE' : {'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' : {'currency' : '€'},
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'BEI.DE' : {'currency' : '€'},
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'EUZ.DE' : {'currency' : '€'},
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'SLAB' : {'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' : {'currency' : '$'},
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'I' : {'currency' : '$'}}
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'I' : {'name': 'IntelSat', 'currency' : '$'}}
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def ema(data, key, alpha=0.5, ic=None):
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def exponential_moving_average(data, key, alpha=0.5, ic=None):
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result = []
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if ic is None:
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r = data[key][0]
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@@ -121,11 +131,11 @@ def ema(data, key, alpha=0.5, ic=None):
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return np.array(result)
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def sma(data, key, window_days, ic=None):
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mem = [0] * window_days
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def moving_average(data, key, window_days, ic=0):
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mem = [ic] * window_days
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result = []
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k=0
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cumsum = 0
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cumsum = ic
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for v in data[key]:
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cumsum += (v - mem[k])
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mem[k] = v
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@@ -137,12 +147,12 @@ def sma(data, key, window_days, ic=None):
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return result
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def sd(data, key, window_days, ic=None):
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mem = [0] * window_days
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def moving_variance(data, key, window_days, ic=0):
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mem = [ic] * window_days
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result = []
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k=0
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cumsum = 0
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data_mean = data[key] - sma(data, key, window_days, ic)
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cumsum = ic
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data_mean = data[key] - moving_average(data, key, window_days, ic)
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for v in data_mean:
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v2 = v * v
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cumsum += (v2 - mem[k])
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@@ -156,6 +166,48 @@ def sd(data, key, window_days, ic=None):
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return result
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def moving_max(data, key, window_days, ic=-1e9):
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mem = [ic] * window_days
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result = []
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k=0
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for v in data[key]:
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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_max = np.max(mem)
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result.append(_max)
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return result
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def moving_min(data, key, window_days, ic=1e9):
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mem = [ic] * window_days
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result = []
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k=0
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for v in data[key]:
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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_min = np.min(mem)
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result.append(_min)
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return result
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def normalize(data, key, ic=None):
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result = []
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if ic is None:
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prev = data[key][0]
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else:
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prev = ic
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for v in data[key]:
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v_n = (v - prev)/prev
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result.append(100*v_n)
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return result
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def colsum(data, keys):
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result = np.zeros(data.shape[0])
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@@ -165,17 +217,17 @@ def colsum(data, keys):
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return result
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def macd(data, key):
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ema_short = ema(data, key, alpha=0.85)
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ema_long = ema(data, key, alpha=0.925)
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ema_short = exponential_moving_average(data, key, alpha=0.85)
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ema_long = exponential_moving_average(data, key, alpha=0.925)
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return ema_short - ema_long
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def bollinger(data, window_days, f=2):
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tp_ser = colsum(data, keys=['high', 'low', 'close']) / 3
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tp_df = tp_ser.to_frame(name='tp')
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stddev = np.array(sd(tp_df, 'tp', window_days))
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stddev = np.array(moving_variance(tp_df, 'tp', window_days))
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mid = np.array(sma(tp_df, key='tp', window_days=window_days))
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mid = np.array(moving_average(tp_df, key='tp', window_days=window_days))
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upper = mid + f * stddev
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lower = mid - f * stddev
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result = pd.DataFrame(upper, index=data.index, columns=['upper'])
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@@ -183,6 +235,31 @@ def bollinger(data, window_days, f=2):
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result['mid'] = mid
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return result
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def agent_buy(name, data, thresh_max=-10, thresh_min=1):
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buy_list = []
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cand = None
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for n in range(0, len(data.index)):
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vmax = data['Qmax'][n]
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vmin = data['Qmin'][n]
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trend = data['macd_fd'][n]
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index = data.index[n]
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if vmin <= thresh_min:
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if vmax <= thresh_max:
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cand = n
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if trend >= 0:
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print ("{}: Buy on {}".format(name, index))
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buy_list.append({'name': name, 'date': index})
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cand = None
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if cand is not None:
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if n - cand >= 5:
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if trend >= 0:
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print("{}: Delayed buy on {}".format(name, data.index[cand]))
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buy_list.append({'name': name, 'date': data.index[cand]})
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cand = None
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return buy_list
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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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@@ -212,7 +289,7 @@ class Title(object):
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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 = True
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fetch = fetch_on_outdated
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except:
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fetch = True
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@@ -246,52 +323,60 @@ class Title(object):
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self.fig.canvas.mpl_connect("motion_notify_event", hover)
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def analyze(self):
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self.data_full['ema'] = ema(self.data_full, key='close', alpha=ema_alpha)
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N = len(self.data_full['close'])
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self.data_full['ema'] = exponential_moving_average(self.data_full, key='close', alpha=ema_alpha)
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self.data_full['macd'] = macd(self.data_full, key='close')
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self.data_full['sma'] = sma(self.data_full, key='close', window_days=sma_days)
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self.data_full['sma'] = moving_average(self.data_full, key='close', window_days=sma_days)
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self.data_full['close_n'] = normalize(self.data_full, key='close')
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self.data_full['min'] = moving_min(self.data_full, key='close_n', window_days=q_days)
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self.data_full['max'] = moving_max(self.data_full, key='close_n', window_days=q_days)
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self.data_full['Qmin'] = self.data_full['close_n'] - self.data_full['min']
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self.data_full['Qmax'] = self.data_full['close_n'] - self.data_full['max']
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self.boll_full = bollinger(self.data_full, window_days=30)
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_macd_curr = self.data_full['macd'][len(self.data_full)-1]
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self._macd_data = self.data_full['macd'][len(self.data_full) - show_range_days:]
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x_r = np.linspace(0, len(self._macd_data), len(self._macd_data))
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y_r = self._macd_data.to_numpy()
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x_r = np.linspace(0, N, N)
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y_r = self.data_full['macd'].to_numpy()
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spl = UnivariateSpline(x_r, y_r)
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spl.set_smoothing_factor(0.5)
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yf = spl(x_r)
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yf_d = spl.derivative()(x_r)
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self._macd_fitted = pd.DataFrame(np.transpose([yf,yf_d]), index=self._macd_data.index, columns=['macd_fitted', 'macd_fitted_d'])
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self.data_full['macd_f'] = pd.DataFrame(np.transpose([yf]), index=self.data_full['macd'].index, columns=['macd_fitted'])
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self.data_full['macd_fd'] = pd.DataFrame(np.transpose([yf_d]), index=self.data_full['macd'].index, columns=['macd_fitted_d'])
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_macd_f_curr = self._macd_fitted['macd_fitted'][len(self._macd_fitted)-1]
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_macd_fd_curr = self._macd_fitted['macd_fitted_d'][len(self._macd_fitted)-1]
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_macd_f_curr = self.data_full['macd_f'][N-1]
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_macd_fd_curr = self.data_full['macd_fd'][N-1]
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return {'macd' : _macd_curr, 'macd_f' : _macd_f_curr, 'macd_fd' : _macd_fd_curr}
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self.data = self.data_full[len(self.data_full) - show_range_days:]
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self.boll = self.boll_full[len(self.boll_full) - show_range_days:]
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return agent_buy(self.symbol, self.data)
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def show(self, figNum=1):
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data = self.data_full[len(self.data_full) - show_range_days:]
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boll = self.boll_full[len(self.boll_full) - show_range_days:]
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self.fig = plt.figure(figNum)
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self.ax = plt.subplot(311)
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self.__plot(data, ['ema', 'sma', 'close'])
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plt.title(self.name)
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self.__plot(self.data, ['min', 'max', 'close_n'])
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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(312)
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boll['upper'].plot()
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boll['mid'].plot()
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boll['lower'].plot()
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# self.boll['upper'].plot()
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# self.boll['mid'].plot()
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# self.boll['lower'].plot()
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self.data['Qmin'].plot()
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self.data['Qmax'].plot()
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plt.legend()
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plt.grid()
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plt.subplot(313)
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self._macd_data.plot()
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self._macd_fitted['macd_fitted'].plot()
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self._macd_fitted['macd_fitted_d'].plot()
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self.data['macd_f'].plot()
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self.data['macd_fd'].plot()
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plt.legend()
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plt.grid()
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@@ -304,15 +389,15 @@ if len(show_symbols) > 0:
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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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print(title.analyze())
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title.analyze()
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title.show(figNum)
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figNum += 1
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else:
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for symbol in symbols:
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print('-----------------------------------------------')
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title = Title(symbol, symbols[symbol])
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title.fetch()
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result = title.analyze()
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if result['macd_f'] > thresh_macd and result['macd_fd'] > 0:
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if len(title.analyze()) > 0:
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title.show(figNum)
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figNum += 1
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