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")
end_date='2002-01-03',
nshares=100)
# In[38]:
f = open('msft6.txt', 'w')
f.write(
str(
microsoft.buy_and_hold(start_date='1999-01-05',
end_date='2002-01-03',
nshares=100)))
f.close()
# In[39]:
microsoft.changepoint_date_analysis()
# In[40]:
f = open('msft7.txt', 'w')
f.write(str(microsoft.changepoint_date_analysis()))
f.close()
# In[41]:
model, future = microsoft.create_prophet_model(days=30)
# In[42]:
f = open('msft8.txt', 'w')
f.write(str(microsoft.create_prophet_model(days=30)))
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)
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
model, future = microsoft.create_prophet_model(days=180)
microsoft.evaluate_prediction()
britishmidland.buy_and_hold(start_date='1999-01-05', end_date='2002-01-03', nshares=100)
# In[17]:
f = open('./BMI/bmi6.txt','w')
f.write(str(britishmidland.buy_and_hold(start_date='1999-01-05', end_date='2002-01-03', nshares=100)))
f.close()
# In[18]:
britishmidland.changepoint_date_analysis()
# In[19]:
f = open('./BMI/bmi7.txt','w')
f.write(str(britishmidland.changepoint_date_analysis()))
f.close()
# In[20]:
model, future = britishmidland.create_prophet_model(days=30)
def stockChangePoint():
print(
"enter the code for the company you want to check the predictions of:")
companyCode = input()
st = Stocker(companyCode)
st.changepoint_date_analysis()
get_ipython().system('pip install quandl')
get_ipython().system('pip install fbprophet')
get_ipython().system('pip install pytrends')
from stocker import Stocker
import quandl
quandl.ApiConfig.api_key = 'AsJgrNdvc_MbnWxwNB4V'
import matplotlib.pyplot as plt
ms = Stocker('MSFT')
ms.plot_stock()
ms.changepoint_date_analysis()
ms.buy_and_hold(start_date='2017-09-01', end_date='2018-03-01', nshares=100)
model, future = ms.create_prophet_model(days=180)
