def gather_data():
"""call financial web scraping API with user defined parameters"""
dir_path file_path = os.path.split(os.path.abspath(__file__))
stocks_path = dir_path + '../data/stocks.json'
with open(stocks_path, 'r') as f:
data = json.load(f)
# nyse100, nasdaq100, snp500 = data['NYSE100'], data['NASDAQ100'], data['SNP500']
# other_stocks = ['AAPL', 'GOOG', 'GPRO', 'TSLA', 'APRN', 'FB', 'NVDA', 'SNAP', 'SPY', 'NFLX', 'AMZN', 'AMD']
# tickers = nyse100 + nasdaq100 + snp500 + other_stocks
# sources = valid_sources()
# used for testing
stocks = ['AAPL', 'TSLA']
# sources = ['seekingalpha', 'bloomberg', 'techcrunch', 'marketwatch', 'yahoofinance']
sources = ['seekingalpha', 'bloomberg']
csv_path = dir_path + '../data/examples.csv'
json_path = dir_path + '../data/links.json'
stats_path = dir_path + '../data/stocker_stats.json'
dm = Stocker(stocks, sources, csv_path, json_path, stats_path=stats_path)
flags = {
'date_checker': True,
'depth': 1,
'validate_url': False,
'length_check': True,
'min_length': 100,
}
dm.stock(shuffle=True, flags=flags)
def listenToClient(client):
data = bytearray()
while True:
datachunk = client.recv(BUFFER)
if not datachunk:
break
data += datachunk
client.close()
data = data.decode().strip('\n').replace(" ", "").split(",")
requestID = data[0]
request_symbol = data[1]
days_into_future = data[2]
s3_client = boto3.client('s3')
obj = s3_client.get_object(
Bucket='elasticbeanstalk-us-west-1-643247086707',
Key=request_symbol + '.csv')
payload = str(obj['Body'].read()).replace(r'\n', '\n')
with open(request_symbol + ".csv", 'w') as f:
f.write(payload)
stock = Stocker(request_symbol, 'CSV', '')
predicted_price = stock.create_prophet_model(
days=int(days_into_future),
resample=False,
symbol=request_symbol,
requestID=requestID)
graph = open(request_symbol + "_" + requestID + ".png", "rb").read()
os.remove(request_symbol + '.csv')
r.set(request_symbol + "_" + requestID, graph)
os.remove(request_symbol + "_" + requestID + ".png")
r.set(request_symbol + "_price_" + requestID, predicted_price)
def stockValueInFuture():
print(
"enter the code for the company you want to check the predictions of:")
companyCode = input()
print("Enter the No. of Days you want to predict the stocks for:")
n = int(input())
st = Stocker(companyCode)
st.create_prophet_model(n)
def cname_test():
passed, failed = Test('cname_test', 1), Test('cname_test', 0)
tickers = ['AAPL', 'GOOG', 'GPRO', 'TSLA', 'NFLX', 'AMZN', 'AMD']
sources = ['seekingalpha']
dm = Stocker(tickers, sources, csv_path=None, json_path=None)
dm.build_queries(depth=2)
q2s = dm.queries[::
2] #starting at index 2 (to get the cname query), get every second query
for q in q2s:
print(q)
def stockValueOverTime():
print(
"enter the code for the company you want to check the predictions of:")
companyCode = input()
print("Enter the start Date(SYNTAX: YYYY-MM-DD)")
start_date = input(datetime.date)
print("Enter the end Date(SYNTAX: YYYY-MM-DD)")
end_date = input(datetime.date)
print("Enter number of shares")
nshares = int(input())
st = Stocker(companyCode)
st.buy_and_hold(start_date, end_date, nshares)
def sytra_follow_fixture(sytra_file_fixture):
# prepare Stocker as stck
stck = Stocker(sytra_file_fixture)
# follow, force: already exists summary.csv
stck.follow_interface(codes=[], force=True)
# set analyconf.toml
(testroot/'4382'/'analyconf.toml').write_text(ac4382)
(testroot/'6890'/'analyconf.toml').write_text(ac6890)
# given style
yield stck
def get_analytics(query_params):
incoming_date_format = "%d-%m-%Y-%H:%M"
new_date_format = "%Y-%m-%d"
start_date = datetime.strptime(query_params["start_date"], incoming_date_format).strftime(new_date_format)
end_date = datetime.strptime(query_params["end_date"], incoming_date_format).strftime(new_date_format)
symbol = query_params["symbol"]
stock = Stocker(symbol)
statistics = microsoft.get_stats(start_date = start_date, end_date = end_date,
stats = ['Open', 'High','Low', 'Daily Change', 'Volume'])
profit = microsoft.buy_and_hold(start_date='2012-03-01', end_date='2012-03-31')
predict = microsoft.create_prophet_model()
response = {
"statusCode" : status,
"body" : {
"open": statistics[0],
"high": statistics[1],
"low": statistics[2],
"daily_change": statistics[3],
"volume": statistics[4],
"profit": profit,
"predict": predict
}
}
return json.dumps(response)
def __init__(self, dt=0.25):
"""Initialize model object.
Parameters
----------
dt : float
Time step in days.
"""
self._string_test = "Hello there"
self.dt = dt
self.numSusceptible = 0
self.numInfected = 0
self.numRecovered = 0
self.INIT_CATTLE_PROBABILITY = 0.02
self.list_cattle = []
self.list_environ = []
self.list_environ.append(RoadEast())
self.list_environ.append(RoadWest())
self.list_environ.append(SaleBarn())
self.list_environ.append(Stocker())
self.list_environ.append(Feedlot())
self.list_environ.append(Abattoir())
self.num_farms = 6
self.farm_cattle_init()
def test_follow(sytra_file_fixture):
stck = Stocker(sytra_file_fixture)
# NO ERROR
# follow 4382, 6890 in prepare
stck.follow_interface(codes=[], force=True)
# create and print sbase
stck.create_sbase()
# save to sytraconf.toml
stck.dump()
def run(root_parser):
namespace = root_parser.parse_args()
if namespace.subcom=='init':
Stocker.stocker_init(**vars(namespace))
return
stck = Stocker(namespace.rootpath)
if namespace.debug:
print(stck.get_follows_tuple(), stck.get_lateststr())
print(namespace)
return
if hasattr(namespace, 'func'): namespace.func( stck, namespace)
if namespace.create: stck.create_sbase()
stck.dump()
def listenToClient(client):
data = bytearray()
while True:
datachunk = client.recv(BUFFER)
if not datachunk:
break
data += datachunk
client.close()
data = data.decode().strip('\n').replace(" ", "").split(",")
requestID = data[0]
request_symbol = data[1]
days_into_future = data[2]
s3_client = boto3.client('s3')
resource = boto3.resource('s3')
my_bucket = resource.Bucket('elasticbeanstalk-us-west-1-643247086707')
objs = list(my_bucket.objects.filter(Prefix=''))
files = list()
for obj in objs:
_, filename = os.path.split(obj.key)
files.append(filename)
if request_symbol + ".csv" in files:
obj = s3_client.get_object(
Bucket='elasticbeanstalk-us-west-1-643247086707',
Key=request_symbol + '.csv')
lastUpdated = obj['LastModified']
if datetime.now() - lastUpdated.replace(tzinfo=None) >= timedelta(
days=7):
Stocker(ticker=request_symbol).stock.to_csv(request_symbol +
'.csv')
my_bucket.upload_file(request_symbol + '.csv',
Key=request_symbol + '.csv')
os.remove(request_symbol + '.csv')
else:
Stocker(ticker=request_symbol).stock.to_csv(request_symbol +
'.csv')
my_bucket.upload_file(request_symbol + '.csv',
Key=request_symbol + '.csv')
os.remove(request_symbol + '.csv')
sock = socket.socket()
sock.connect((DM_SERVICE, DM_PORT))
msg = requestID + "," + request_symbol + "," + days_into_future
sock.send(msg.encode('ascii'))
sock.close()
def stock_info():
## Get historical data on stock
##print("Data provided for free by IEX. \nView IEX’s Terms of Use at https://iextrading.com/api-exhibit-a/")
ticker = get_stock()
try:
stock = Stocker(ticker)
except:
print("Ticker not found.")
print("\n\nGeneral info on historical data of stock and movement in the past\n\n")
time.sleep(5)
stock.plot_stock()
## Object to return for predictive model
#stock_obj =
return stock
def sytra_file_fixture():
if testroot.exists():
shutil.rmtree(str(testroot))
# create directory and sytraconf.toml
Stocker.stocker_init(rootdir=str(testroot), daystr='2020-07-10')
# directory for holidays
(testroot/'holidays'/'2020.csv').write_text(h2020)
(testroot/'holidays'/'2021.csv').write_text(h2021)
# directory for prepare
(testroot/'prepare'/'4382.csv').write_text(s4382)
(testroot/'prepare'/'6890.csv').write_text(s6890)
# sbase.csv
(testroot/'summary.csv').write_text(sum0713)
# load style
yield str(testroot)
def __init__(self, dt=0.25, init_extra_weight=0.0):
"""Initialize model object.
Parameters
----------
dt : float
Time step in days.
init_extra_weight : float
Adds this many pounds to the initialization of each cattle in
order to speed-up the simulation.
"""
self._string_test = "Hello there"
self.dt = dt
self.numSusceptible = 0
self.numInfected = 0
self.numRecovered = 0
self.cumulativeInfected = 0
self.INIT_CATTLE_PROBABILITY = 0.02
self._init_extra_weight = init_extra_weight
self.plot_figure = None
self.plot_axes = None
self.plot_image = None
self.list_cattle = []
self.list_farms = []
self.salebarn = SaleBarn()
self.stocker = Stocker()
self.feedlot = Feedlot()
self.abattoir = Abattoir()
self.roadeast = RoadEast(adjacent_salebarn=self.salebarn)
self.roadwest = RoadWest(adjacent_salebarn=self.salebarn)
#- Linkages for multi-way movement between certain regions:
self.salebarn.adjacent_stocker = self.stocker
self.salebarn.adjacent_feedlot = self.feedlot
self.stocker.adjacent_salebarn = self.salebarn
self.feedlot.adjacent_salebarn = self.salebarn
self.feedlot.adjacent_abattoir = self.abattoir
self.num_farms = 6
self.farm_cattle_init()
from stocker import Stocker import matplotlib.pyplot as plt microsoft = Stocker(ticker='MSFT') stock_history = microsoft.stock # microsoft.plot_stock(start_date='2001-01-05', end_date='2002-02-05', stats=["Adj. Close"], plot_type="basic") # microsoft.buy_and_hold(start_date='2001-01-05', end_date='2002-02-05', nshares=200) model, model_data = microsoft.create_prophet_model() model.plot_components(model_data) plt.show() print(microsoft.weekly_seasonality) microsoft.weekly_seasonality = True print(microsoft.weekly_seasonality) model, model_data = microsoft.create_prophet_model(days=0) model.plot_components(model_data) plt.show() microsoft.weekly_seasonality = False microsoft.changepoint_date_analysis() model, future = microsoft.create_prophet_model(days=180)
import stocker
from stocker import Stocker
stock_name = 'AAPL'
s = Stocker(stock_name)
#s.plot_stock()
#stock_data = stocker.get_data.main(stock_name, years=1)
#stock_history = s.stock
#print(stock_history.tail())
prediction, a, model = stocker.predict.tomorrow('GOOG', plot=True)
#E = [17.95, 40.85, 1.88, 11.92, 177.70, 24.11]
#p = model.predict(E)
#print(prediction)
#stock_names = ['GAZP.ME', 'TSLA', 'BP', 'AAPL', 'GOOG', 'SBER.ME']
#E = [17.95, 40.85, 1.88, 11.92, 177.70, 24.11]
!git clone 'https://github.com/WillKoehrsen/Data-Analysis.git'
os.chdir('./Data-Analysis/stocker')
!pip install -U quandl numpy pandas fbprophet matplotlib pytrends pystan
import stocker
from stocker import Stocker
data = pd.DataFrame()
stocks = ['AAPL', 'ADBE', 'SYMC', 'EBAY', 'MSFT', 'QCOM', 'HPQ', 'JNPR', 'AMD', 'IBM']
apple = Stocker('AAPL')
df = apple.make_df('1990-12-12', '2016-12-12')
df = df.set_index(['Date'])
apple_closes = df['Adj. Close']
df.head()
apple_closes.head()
for ticker in stocks:
name = str(ticker)
print(name)
s = Stocker(name)
df = s.make_df('2000-12-12', '2016-12-12')
df = df.set_index(['Date'])
data[name] = df['Adj. Close']
from stocker import Stocker
amazon = Stocker('AMZN')
amazon.plot_stock()
# predict days into the future
model, model_data = amazon.create_prophet_model(days=90)
amazon.evaluate_prediction()
def GetStockPrediction(csvfile,ticker,inifile,litemode=False):
### MAKE HISTORY GRAPH.
hstg = 30 # number of days
# Define files and filetypes
ext = ".pdf"
ModZ =[str(stocksfolder+ticker+".HighLowClose" + ext), #0
str(stocksfolder+ticker+".ChangeVolume"+ext), #1
str(stocksfolder+ticker+".ChangePoint"+ext), #2
# str(stocksfolder+ticker+".ChangePointTrends"+ext),#3
str(stocksfolder+ticker+".evaluate_prediction"+ext),#4
str(stocksfolder+ticker+".TrendsA"+ext),#5
str(stocksfolder+ticker+".Naive" + str(hstg) + ext)]
#6
StockFiles=[str(stocksfolder+ticker+".HighLowClose" + ext), #0
str(stocksfolder+ticker+".ChangeVolume"+ext), #1
str(stocksfolder+ticker+".ChangePoint"+ext), #2
str(stocksfolder+ticker+".ChangePointTrends"+ext),#3
str(stocksfolder+ticker+".evaluate_prediction"+ext),#4
str(stocksfolder+ticker+".TrendsA"+ext),#5
str(stocksfolder+ticker+".Naive" + str(hstg) + ext),#6
# ACTUAL MACHINE LEARNING BEGINS
str(stocksfolder+ticker+".linear_regression_01"+ext)]#7
# print(csvfile)
# df = pd.read_csv(csvfile)
## START ANALSYS.
Months = str((today - datetime.timedelta(days=hstg)).date())
### Stocker is initialized and will retrieve the entire stock's data as it is read in the file.
stock = Stocker(ticker=ticker,
exchange='CSV',
csv_repository='stocks')
print(stock.stock.head())
plt.clf()
### plot_stock will plot the stock's (High, Low, Close) recent history. see 'hstg' for number of days
stock.plot_stock(start_date = str(Months),
stats = ['high', 'low', 'close'],
plot_type='hlc',
filename=StockFiles[0])
plt.clf()
### plot_stock will plot the Stock's (Daily Change, Volume) recent history. see 'hstg' for number of days
stock.plot_stock(start_date = str(Months) ,
stats = ['Daily Change', 'volume'],
plot_type='pct',
filename=StockFiles[1])
plt.clf()
stock.changepoint_date_analysis(filename=StockFiles[2])
plt.clf()
# stock.buy_and_hold(start_date=str(Months),nshares=1,filename=str(stocksfolder+ticker+".PredictedBuyHold"+ext)) # This function is broken.
# plt.clf()
stock.evaluate_prediction(start_date=str(Months),nshares=1,filename=StockFiles[4])
plt.clf()
## START BASIC PREDICTIONS BASED ON ANALYSIS
model, model_data = stock.create_prophet_model()
model.plot_components(model_data)
plt.savefig(StockFiles[5])
plt.clf()
stock.predict_future(days=hstg,filename=StockFiles[6])
plt.clf()
# START MORE ADVANCED PREDICTONS BASED ON DEEP LEARNING.
if litemode:
pass
else:
### changepoint_date_analysis is looking through historical data of the past 3 years to find the optimal time to buy or sell a stock.
print(inifile)
search_terms = inifile["STOCK PROFILE"]["search_terms"]
# print(search_terms)
bountylist = search_terms.split(",")
# print(bountylist)
stock.changepoint_date_analysis(search=bountylist,filename=StockFiles[3])
plt.clf()
# LINEAR REGRESSION 01
stock.Stocker_linear_regression(days=hstg, filename=StockFiles[7])
plt.clf()
## Merge files into a pdf.
if litemode:
ModZ = ModZ; print(ModZ)
else:
ModZ = StockFiles[:]; print(ModZ)
# thelimit = len(ModZ)
Merge_PDFS(ModZ,reportsfolder+ticker+"._REPORT.PDF")
import pdb
import mymodule
#Stocker Library Code Example
from stocker import Stocker
microsoft = Stocker('MSFT')
stock_history = microsoft.stock
stock_history.head()
microsoft.plot_stock()
microsoft.plot_stock(start_date = '2000-01-03', end_date = '2018-01-16', stats ['Daily Change', 'Adj.Volume'], plot_type='pct')
microsoft.buy_and_hold(start_date = '1986-03-13', end_date='2018-01-16', nshares=100)
model, model_data = microsoft.create_prophet_model()
model.plot_components(model_data)
plt.show()
print(micorsoft.weekly_seasonality)
microsoft.weekly_seasonality = True
print(microsoft.weekly_seasonality)
#only shows data in the first 80% of data
microsoft.changepoint_date_analysis()
#This will need to add a library of search terms related to the stocks information
microsoft.changepoint_date_analysis(search = 'Microsoft profit')
microsoft.changepoint_date_analysis(search = 'Microsoft Office')
#Future predictions
def get_stocker(names):
stocker_data = []
for x in names:
stock = Stocker(x)
stocker_data.append(stock)
return stocker_data
# -*- coding: utf-8 -*- # @Author : Ryan # @Time : 2021/9/29 12:39 # @Software : PyCharm # @Description : import pandas as pd from prophet import Prophet from stocker import Stocker microsoft = Stocker(ticker='MSFT') print(microsoft)
from stocker import Stocker
hp = Stocker("HPQ")
stock_history = hp.stock
print(stock_history.head())
model, model_data = hp.create_prophet_model()
model.plot_components(model_data)
plt.show()
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 7 22:57:31 2019
@author: Huntrer
"""
from stocker import Stocker
microsoft = Stocker('MSFT')
# A method (function) requires parentheses
microsoft.plot_stock()
#!/usr/bin/env python
import sys
from stocker import Stocker
if __name__ == "__main__":
args = sys.argv[1:]
symbol = args[0]
stocker = Stocker()
price, time_of_price = stocker.get_stock_price(symbol)
print "{symbol}: ${price}, Time of price: {time_of_price}".format(symbol=symbol, price=price, time_of_price=time_of_price)
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 29 15:26:09 2021
@author: jeti8
"""
from stocker import Stocker
amazon = Stocker('NKE')
model, model_data = amazon.create_prophet_model(days=90)
amazon.evaluate_prediction(nshares=1000)
amazon.predict_future(days=10)
amazon.predict_future(days=100)
# coding: utf-8
# In[23]:
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
# In[24]:
from stocker import Stocker
# In[25]:
microsoft = Stocker('MSFT')
# In[26]:
stock_history = microsoft.stock
stock_history.head()
# In[27]:
f = open('msft1.txt', 'w')
f.write(str(stock_history.head()))
f.close()
# In[28]:
microsoft.plot_stock()
# In[29]:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style
import matplotlib as mpl
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20, 10
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from fastai.structured import add_datepart
import tensorflow as tf
from tensorflow.keras import layers
from sklearn import neighbors
from sklearn.model_selection import GridSearchCV
from pandas.util.testing import assert_frame_equal
goog = Stocker('GOOGL')
goog.plot_stock()
# Create model
model, model_data = goog.create_prophet_model(days=90)
goog.evaluate_prediction()
# Optimize the model
goog.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2])
goog.changepoint_prior_validation(start_date='2016-01-04',
end_date='2017-01-03',
changepoint_priors=[0.001, 0.05, 0.1, 0.2])
# Evaluate the new model
goog.evaluate_prediction()
print(goog.evaluate_prediction(nshares=1000))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 1 20:43:37 2018
@author: https://towardsdatascience.com/stock-prediction-in-python-b66555171a2
"""
#%%
from stocker import Stocker
teradyne = Stocker('TER')
#%%
model, model_data = teradyne.create_prophet_model(days=180)
#%%
teradyne.stock.head()
#%%
teradyne.evaluate_prediction(nshares=100)
#-------------
#%%
twtr = Stocker('TWTR')
#%%
model, model_data = twtr.create_prophet_model(days=200)
#%%
twtr.stock.tail()
sids = ['2816']
for sid in sids:
start = '2015-01-01'
#end=str(today).split('-')[0]+'-'+str(today).split('-')[1]
end = '2018-12-26'
df = st3.GetSidDataFrame(sid, start, end)
df['date'] = pd.to_datetime(df['date'])
df.set_index("date", inplace=True)
df = df['close']
price = df.squeeze()
#print(price.head())
#print(price.index)
Target = Stocker(price, name=sid)
#Target.changepoint_prior_validation(start_date ='2015-12-03',end_date ='2018-12-21',changepoint_priors = [0.3,0.4,0.45,0.5,0.6])
#Target.plot_stock()
#原始參數預測
#model, model_data = Target.create_prophet_model(days=10)
#原始參數回測
#Target.evaluate_prediction()
#Target.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2])
#Target.changepoint_prior_validation(start_date='2015-01-07', end_date='2018-12-21', changepoint_priors=[0.05,0.1,0.2,0.3,0.4])
#調整參數為0.4
