def setup_db_for_hist_prices_storage(self, stock_sym_list):
""" Get the price and dividend history and store them to the database for the specified stock sym list.
The length of time depends on the date_interval specified.
Connection to database is assuemd to be set.
For one time large dataset (where the hist data is very large)
Args:
stock_sym_list (list): list of stock symbol.
"""
## set the class for extraction
histdata_extract = YFHistDataExtr()
histdata_extract.set_interval_to_retrieve(360*5)# assume for 5 years information
histdata_extract.enable_save_raw_file = 0
for sub_list in self.break_list_to_sub_list(stock_sym_list):
print ('processing sub list', sub_list)
histdata_extract.set_multiple_stock_list(sub_list)
histdata_extract.get_hist_data_of_all_target_stocks()
histdata_extract.removed_zero_vol_fr_dataset()
## save to one particular funciton
#save to sql -- hist table
histdata_extract.processed_data_df.to_sql(self.hist_data_tablename, self.con, flavor='sqlite',
schema=None, if_exists='append', index=True,
index_label=None, chunksize=None, dtype=None)
#save to sql -- div table
histdata_extract.all_stock_div_hist_df.to_sql(self.divdnt_data_tablename, self.con, flavor='sqlite',
schema=None, if_exists='append', index=True,
index_label=None, chunksize=None, dtype=None)
self.close_db()
def retrieve_hist_data_fr_web(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
self.histdata_ext = YFHistDataExtr()
self.histdata_ext.set_interval_to_retrieve(200)
self.set_stocklist(self.stocklist)
self.histdata_ext.get_hist_data_of_all_target_stocks()
self.raw_all_stock_df = self.histdata_ext.all_stock_df
def setup_db_for_hist_prices_storage(self,stock_sym_list,interval):
#get price history
histdata_extract = YFHistDataExtr()
histdata_extract.set_interval_to_retrieve(360*interval)
histdata_extract.enable_save_raw_file=0
for sub_list in self.break_list_to_sub_list(stock_sym_list):
print ('processing sub list', sub_list)
histdata_extract.set_multiple_stock_list(sub_list)
histdata_extract.get_hist_data_of_all_target_stocks()
histdata_extract.removed_zero_vol_fr_dataset()
# save to SQL table
histdata_extract.processed_data_df.to_sql(self.hist_data_tablename, self.con, flavor='sqlite',
schema=None, if_exists='append', index=True,
index_label=None, chunksize=None, dtype=None)
self.close_db()
def calculate_bands(ticker_symbol):
# Delete the old bands image for this picture
os.system("rm ./static/pictures/%s.png" % ticker_symbol)
data_ext = YFHistDataExtr()
data_ext.set_interval_to_retrieve(400) #in days
data_ext.set_multiple_stock_list([str(ticker_symbol)])
data_ext.get_hist_data_of_all_target_stocks()
# convert the date column to date object
data_ext.all_stock_df['Date'] = pandas.to_datetime(
data_ext.all_stock_df['Date'])
temp_data_set = data_ext.all_stock_df.sort(
'Date', ascending=True) #sort to calculate the rolling mean
temp_data_set['20d_ma'] = pandas.rolling_mean(temp_data_set['Adj Close'],
window=5)
temp_data_set['50d_ma'] = pandas.rolling_mean(temp_data_set['Adj Close'],
window=50)
temp_data_set['Bol_upper'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=80) + 2 * pandas.rolling_std(
temp_data_set['Adj Close'], 80, min_periods=80)
temp_data_set['Bol_lower'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=80) - 2 * pandas.rolling_std(
temp_data_set['Adj Close'], 80, min_periods=80)
temp_data_set['Bol_BW'] = (
(temp_data_set['Bol_upper'] - temp_data_set['Bol_lower']) /
temp_data_set['20d_ma']) * 100
temp_data_set['Bol_BW_200MA'] = pandas.rolling_mean(
temp_data_set['Bol_BW'], window=50) #cant get the 200 daa
temp_data_set['Bol_BW_200MA'] = temp_data_set['Bol_BW_200MA'].fillna(
method='backfill') ##?? ,may not be good
temp_data_set['20d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=20)
temp_data_set['50d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=50)
data_ext.all_stock_df = temp_data_set.sort(
'Date', ascending=False) #revese back to original
data_ext.all_stock_df.plot(
x='Date',
y=['Adj Close', '20d_ma', '50d_ma', 'Bol_upper', 'Bol_lower'])
#data_ext.all_stock_df.plot(x='Date', y=['Bol_BW','Bol_BW_200MA' ])
plt.savefig('./static/pictures/%s.png' % ticker_symbol)
return temp_data_set['Adj Close'], temp_data_set[
'Bol_lower'], temp_data_set['20d_ma']
def retrieve_hist_data_fr_web(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
self.histdata_ext = YFHistDataExtr()
self.histdata_ext.set_interval_to_retrieve(200)
self.set_stocklist(self.stocklist)
self.histdata_ext.get_hist_data_of_all_target_stocks()
self.raw_all_stock_df = self.histdata_ext.all_stock_df
print "Basic Filtering of stock data."
ss = InfoBasicFilter(r'c:\data\temp\temp_stockdata.csv')
ss.set_criteria_type('basic')
ss.get_all_criteria_fr_file()
ss.process_criteria()
print "stocks left after basic filtering: ", len(ss.modified_df)
ss.modified_df.to_csv(final_store_filename, index = False)
print "Basic Filtering of stock data -- Done. \n"
if 'c_backup' in partial_run:
## testing, to remove after this
#ss.modified_df = ss.modified_df.head()
## 3 days trends data
print "Getting Trends data"
trend_ext = YFHistDataExtr()
trend_ext.set_interval_to_retrieve(365*5)
trend_ext.enable_save_raw_file = 0
trend_ext.set_multiple_stock_list(list(ss.modified_df['SYMBOL']))
trend_ext.run_all_hist_data()
full_stock_data_df = pandas.merge(ss.modified_df, trend_ext.all_stock_combined_post_data_df, on = 'SYMBOL', how ='left')
print "Getting Trends data -- Done. \n"
#full_stock_data_df.to_csv(r'c:\data\full_oct21.csv', index = False)
if 'c_pre' in partial_run:
""" Update the database """
print 'Updating the database with latest hist price.'
datastore = FinanceDataStore(db_full_path)
stock_list = list(ss.modified_df['SYMBOL'])
datastore.scan_and_input_recent_prices(stock_list,5)
def setup_db_for_hist_prices_storage(self, stock_sym_list):
""" Get the price and dividend history and store them to the database for the specified stock sym list.
The length of time depends on the date_interval specified.
Connection to database is assuemd to be set.
For one time large dataset (where the hist data is very large)
Args:
stock_sym_list (list): list of stock symbol.
"""
## set the class for extraction
histdata_extr = YFHistDataExtr()
histdata_extr.set_interval_to_retrieve(360*5)# assume for 5 years information
histdata_extr.enable_save_raw_file = 0
for sub_list in self.break_list_to_sub_list(stock_sym_list):
## re -initalize the df
histdata_extr.all_stock_df = pandas.DataFrame()
histdata_extr.processed_data_df = pandas.DataFrame()
histdata_extr.all_stock_div_hist_df = pandas.DataFrame()
print 'processing sub list', sub_list
histdata_extr.set_multiple_stock_list(sub_list)
histdata_extr.get_hist_data_of_all_target_stocks()
histdata_extr.removed_zero_vol_fr_dataset()
## save to one particular funciton
#save to sql -- hist table
histdata_extr.processed_data_df.to_sql(self.hist_data_tablename, self.con, flavor='sqlite',
schema=None, if_exists='append', index=True,
index_label=None, chunksize=None, dtype=None)
#save to sql -- div table
histdata_extr.all_stock_div_hist_df.to_sql(self.divdnt_data_tablename, self.con, flavor='sqlite',
schema=None, if_exists='append', index=True,
index_label=None, chunksize=None, dtype=None)
self.close_db()
def setup_db_for_hist_prices_storage(self, stock_sym_list, interval):
#get price history
histdata_extract = YFHistDataExtr()
histdata_extract.set_interval_to_retrieve(360 * interval)
histdata_extract.enable_save_raw_file = 0
for sub_list in self.break_list_to_sub_list(stock_sym_list):
print('processing sub list', sub_list)
histdata_extract.set_multiple_stock_list(sub_list)
histdata_extract.get_hist_data_of_all_target_stocks()
histdata_extract.removed_zero_vol_fr_dataset()
# save to SQL table
histdata_extract.processed_data_df.to_sql(self.hist_data_tablename,
self.con,
flavor='sqlite',
schema=None,
if_exists='append',
index=True,
index_label=None,
chunksize=None,
dtype=None)
self.close_db()
class TechAnalysisAdd(object):
def __init__(self, stocklist):
""" Intialize hist data extr.
Set the stocklist and get all hist data.
Args:
stocklist (list): list of stock symbol. For Singapore stocks, best to include the .SI
"""
## stocklist
self.stocklist = stocklist
## user parameters
self.get_fr_database = 0 # 1 will pull data from database, 0 will get directly get from web.
self.database_path = r'C:\data\stock_sql_db\stock_hist.db'
## Save parameters.
self.raw_all_stock_df = pandas.DataFrame()
self.histdata_indiv_stock = object() # for single stock handling.
self.histdata_combined = pandas.DataFrame()
self.processed_histdata_combined = object()
def set_stocklist(self, stocklist):
""" Method to set the stocklist for raw data pulling.
Set to self.histdata_ext.
Args:
stocklist (list): list of stock symbol. For Singapore stocks, best to include the .SI
"""
self.histdata_ext.set_multiple_stock_list(stocklist)
def enable_pull_fr_database(self):
""" Set the self.get_fr_database parameter so it will pull from database.
set to self.get_fr_database.
"""
self.get_fr_database = 1
def retrieve_hist_data(self):
""" Retrieve all the hist data.
"""
if not self.get_fr_database:
self.retrieve_hist_data_fr_web()
else:
self.retrieve_hist_data_fr_database()
self.raw_all_stock_df['Date'] = pandas.to_datetime(
self.raw_all_stock_df['Date'])
def retrieve_hist_data_fr_database(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
c = FinanceDataStore(self.database_path)
c.retrieve_hist_data_fr_db(self.stocklist, 0)
c.extr_hist_price_by_date(200)
self.raw_all_stock_df = c.hist_price_df
def retrieve_hist_data_fr_web(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
self.histdata_ext = YFHistDataExtr()
self.histdata_ext.set_interval_to_retrieve(200)
self.set_stocklist(self.stocklist)
self.histdata_ext.get_hist_data_of_all_target_stocks()
self.raw_all_stock_df = self.histdata_ext.all_stock_df
def add_Bollinger_parm(self):
""" Add the list of Bolinger Parm.
Set to self.histdata_indiv_stock.
"""
temp_data_set = self.histdata_indiv_stock.sort(
'Date', ascending=True) #sort to calculate the rolling mean
temp_data_set['20d_ma'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=20)
temp_data_set['50d_ma'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=50)
temp_data_set['Bol_upper'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=20) + 2 * pandas.rolling_std(
temp_data_set['Adj Close'], 20, min_periods=20)
temp_data_set['Bol_lower'] = pandas.rolling_mean(
temp_data_set['Adj Close'], window=20) - 2 * pandas.rolling_std(
temp_data_set['Adj Close'], 20, min_periods=20)
temp_data_set['Bol_BW'] = (
(temp_data_set['Bol_upper'] - temp_data_set['Bol_lower']) /
temp_data_set['20d_ma']) * 100
temp_data_set['Bol_BW_200MA'] = pandas.rolling_mean(
temp_data_set['Bol_BW'], window=50) #cant get the 200 daa
temp_data_set['Bol_BW_200MA'] = temp_data_set['Bol_BW_200MA'].fillna(
method='backfill') ##?? ,may not be good
temp_data_set['20d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=20)
temp_data_set['50d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=50)
self.histdata_indiv_stock = temp_data_set.sort(
'Date', ascending=False) #revese back to original
def add_MACD_parm(self):
""" Include the MACD parm.
"""
temp_data_set = self.histdata_indiv_stock.sort('Date', ascending=True)
temp_data_set['12d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=12)
temp_data_set['26d_exma'] = pandas.ewma(temp_data_set['Adj Close'],
span=26)
temp_data_set['MACD'] = temp_data_set['12d_exma'] - temp_data_set[
'26d_exma'] #12-26
temp_data_set['MACD_signalline'] = pandas.rolling_mean(
temp_data_set['MACD'], window=9)
temp_data_set['MACD_hist'] = temp_data_set['MACD'] - temp_data_set[
'MACD_signalline']
self.histdata_indiv_stock = temp_data_set.sort(
'Date', ascending=False) #revese back to original
def add_pivot_point(self):
""" Getting the pivot pt based on the last entry. Same operation as other add function.
Cater for one stock at a time. Assume the first entry is the latest date.
The returning will be the self.histdata_indiv_stock with the addional parameter
"""
temp = self.histdata_indiv_stock.head(2)[1:2]
try:
pivot_value = list(
(temp['High'] + temp['Low'] + temp['Adj Close']) / 3)[0]
R1_value = (pivot_value * 2) - list(temp['Low'])[0]
S1_value = (pivot_value * 2) - list(temp['High'])[0]
except:
pivot_value = 0
R1_value = 0
S1_value = 0
self.histdata_indiv_stock['Pivot'] = pivot_value
self.histdata_indiv_stock['S1'] = S1_value
self.histdata_indiv_stock['R1'] = R1_value
def add_average_vol(self):
""" Getting the average volume over 200 days.
"""
temp_group = self.histdata_indiv_stock.groupby('SYMBOL')
try:
avg_volume = temp_group['Volume'].agg('mean').values[0]
except:
avg_volume = 0
self.histdata_indiv_stock['Avg_volume_200d'] = avg_volume
self.histdata_indiv_stock['Avg_volume_above30per'] = (
self.histdata_indiv_stock['Volume'] > 1.3 * avg_volume)
self.histdata_indiv_stock['Avg_volume_above70per'] = (
self.histdata_indiv_stock['Volume'] > 1.7 * avg_volume)
def add_analysis_parm(self):
""" Add all the different analysis data to the data set.
"""
self.histdata_combined = pandas.DataFrame()
for symbol in self.stocklist:
self.histdata_indiv_stock = self.raw_all_stock_df[
self.raw_all_stock_df['SYMBOL'] == symbol]
self.add_Bollinger_parm()
self.add_MACD_parm()
self.add_pivot_point()
self.add_average_vol()
if len(self.histdata_combined) == 0:
self.histdata_combined = self.histdata_indiv_stock
else:
self.histdata_combined = self.histdata_combined.append(
self.histdata_indiv_stock)
def bollinger_plots(self):
""" Plot the bollinger plots for each stocks.
Used self.histdata_indiv_stock for data passing.
Pivot is added to here
"""
self.histdata_indiv_stock.plot(x='Date',
y=[
'Adj Close', '20d_ma', '50d_ma',
'Bol_upper', 'Bol_lower', 'Pivot',
'S1', 'R1'
])
self.histdata_indiv_stock.plot(x='Date', y=['Bol_BW', 'Bol_BW_200MA'])
plt.show()
def MACD_plots(self):
""" Plot the MACD plots for each stocks.
Used self.histdata_indiv_stock for data passing.
"""
self.histdata_indiv_stock.plot(x='Date',
y=['MACD', 'MACD_signalline'],
ylim=[-0.3, 0.3])
#self.histdata_indiv_stock.plot(x='Date', y=['MACD_hist' ],kind='bar')#problem with this plot
plt.show()
def analysis_plot_for_tgt_sym(self, symbol):
""" Get all analysis plot for target symbol.
For each symbol, set to self.histdata_indiv_stock
Args:
symbol (str): stock symbol.
Type of plots to include
"""
self.histdata_indiv_stock = self.histdata_combined[
self.histdata_combined['SYMBOL'] == symbol]
self.bollinger_plots()
self.MACD_plots()
def get_most_current_dataset(self):
""" Get the first (or most current) data for every stock.
Additional parm --> cross the bollinger band
cross the 50 days ex moving avg
Bol width - Bol 200 (percentage)
MACD hist (percentage)
"""
self.processed_histdata_combined = self.histdata_combined.groupby(
"SYMBOL").first().reset_index()
def add_response_trigger(self):
""" Add series of basic trigger point for basic technical analysis.
"""
tar_data = self.processed_histdata_combined
tar_data[
'Above_Boll_upper'] = tar_data['Adj Close'] > tar_data['Bol_upper']
tar_data[
'Below_Boll_lower'] = tar_data['Bol_lower'] > tar_data['Adj Close']
tar_data['price_above_50dexm'] = tar_data['Adj Close'] > tar_data[
'50d_exma']
tar_data['20dexm_above_50dexm'] = tar_data['20d_exma'] > tar_data[
'50d_exma']
self.processed_histdata_combined = tar_data
def display_avaliable_info_GUI(self):
""" For plot display in GUI.
"""
stock_choice = self.histdata_combined['SYMBOL'].drop_duplicates(
).tolist()
while True:
choice = easygui.choicebox(choices=stock_choice)
if choice == None:
return
else:
self.analysis_plot_for_tgt_sym(choice)
class TechAnalysisAdd(object):
def __init__(self,stocklist):
""" Intialize hist data extr.
Set the stocklist and get all hist data.
Args:
stocklist (list): list of stock symbol. For Singapore stocks, best to include the .SI
"""
## stocklist
self.stocklist = stocklist
## user parameters
self.get_fr_database = 0 # 1 will pull data from database, 0 will get directly get from web.
self.database_path = r'C:\data\stock_sql_db\stock_hist.db'
## Save parameters.
self.raw_all_stock_df = pandas.DataFrame()
self.histdata_indiv_stock = object() # for single stock handling.
self.histdata_combined = pandas.DataFrame()
self.processed_histdata_combined = object()
def set_stocklist(self, stocklist):
""" Method to set the stocklist for raw data pulling.
Set to self.histdata_ext.
Args:
stocklist (list): list of stock symbol. For Singapore stocks, best to include the .SI
"""
self.histdata_ext.set_multiple_stock_list(stocklist)
def enable_pull_fr_database(self):
""" Set the self.get_fr_database parameter so it will pull from database.
set to self.get_fr_database.
"""
self.get_fr_database = 1
def retrieve_hist_data(self):
""" Retrieve all the hist data.
"""
if not self.get_fr_database:
self.retrieve_hist_data_fr_web()
else:
self.retrieve_hist_data_fr_database()
self.raw_all_stock_df['Date'] = pandas.to_datetime( self.raw_all_stock_df['Date'])
def retrieve_hist_data_fr_database(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
c = FinanceDataStore(self.database_path)
c.retrieve_hist_data_fr_db(self.stocklist,0)
c.extr_hist_price_by_date(200)
self.raw_all_stock_df = c.hist_price_df
def retrieve_hist_data_fr_web(self):
""" Retrieve the hist data from web using yahoo_finance_historical_data_extract module.
Set to self.raw_all_stock_df
"""
self.histdata_ext = YFHistDataExtr()
self.histdata_ext.set_interval_to_retrieve(200)
self.set_stocklist(self.stocklist)
self.histdata_ext.get_hist_data_of_all_target_stocks()
self.raw_all_stock_df = self.histdata_ext.all_stock_df
def add_Bollinger_parm(self):
""" Add the list of Bolinger Parm.
Set to self.histdata_indiv_stock.
"""
temp_data_set = self.histdata_indiv_stock.sort('Date',ascending = True ) #sort to calculate the rolling mean
temp_data_set['20d_ma'] = pandas.rolling_mean(temp_data_set['Adj Close'], window=20)
temp_data_set['50d_ma'] = pandas.rolling_mean(temp_data_set['Adj Close'], window=50)
temp_data_set['Bol_upper'] = pandas.rolling_mean(temp_data_set['Adj Close'], window=20) + 2* pandas.rolling_std(temp_data_set['Adj Close'], 20, min_periods=20)
temp_data_set['Bol_lower'] = pandas.rolling_mean(temp_data_set['Adj Close'], window=20) - 2* pandas.rolling_std(temp_data_set['Adj Close'], 20, min_periods=20)
temp_data_set['Bol_BW'] = ((temp_data_set['Bol_upper'] - temp_data_set['Bol_lower'])/temp_data_set['20d_ma'])*100
temp_data_set['Bol_BW_200MA'] = pandas.rolling_mean(temp_data_set['Bol_BW'], window=50)#cant get the 200 daa
temp_data_set['Bol_BW_200MA'] = temp_data_set['Bol_BW_200MA'].fillna(method='backfill')##?? ,may not be good
temp_data_set['20d_exma'] = pandas.ewma(temp_data_set['Adj Close'], span=20)
temp_data_set['50d_exma'] = pandas.ewma(temp_data_set['Adj Close'], span=50)
self.histdata_indiv_stock = temp_data_set.sort('Date',ascending = False ) #revese back to original
def add_MACD_parm(self):
""" Include the MACD parm.
"""
temp_data_set = self.histdata_indiv_stock.sort('Date',ascending = True )
temp_data_set['12d_exma'] = pandas.ewma(temp_data_set['Adj Close'], span=12)
temp_data_set['26d_exma'] = pandas.ewma(temp_data_set['Adj Close'], span=26)
temp_data_set['MACD'] = temp_data_set['12d_exma'] - temp_data_set['26d_exma'] #12-26
temp_data_set['MACD_signalline'] = pandas.rolling_mean(temp_data_set['MACD'], window=9)
temp_data_set['MACD_hist'] = temp_data_set['MACD'] - temp_data_set['MACD_signalline']
self.histdata_indiv_stock = temp_data_set.sort('Date',ascending = False ) #revese back to original
def add_pivot_point(self):
""" Getting the pivot pt based on the last entry. Same operation as other add function.
Cater for one stock at a time. Assume the first entry is the latest date.
The returning will be the self.histdata_indiv_stock with the addional parameter
"""
temp = self.histdata_indiv_stock.head(2)[1:2]
try:
pivot_value = list((temp['High'] + temp['Low'] + temp['Adj Close'])/3)[0]
R1_value = (pivot_value*2) - list(temp['Low'])[0]
S1_value = (pivot_value*2) - list(temp['High'])[0]
except:
pivot_value = 0
R1_value = 0
S1_value = 0
self.histdata_indiv_stock['Pivot'] = pivot_value
self.histdata_indiv_stock['S1'] = S1_value
self.histdata_indiv_stock['R1'] = R1_value
def add_average_vol(self):
""" Getting the average volume over 200 days.
"""
temp_group = self.histdata_indiv_stock.groupby('SYMBOL')
try:
avg_volume = temp_group['Volume'].agg('mean').values[0]
except:
avg_volume = 0
self.histdata_indiv_stock['Avg_volume_200d'] = avg_volume
self.histdata_indiv_stock['Avg_volume_above30per'] = (self.histdata_indiv_stock['Volume']> 1.3*avg_volume)
self.histdata_indiv_stock['Avg_volume_above70per'] = (self.histdata_indiv_stock['Volume']> 1.7*avg_volume)
def add_analysis_parm(self):
""" Add all the different analysis data to the data set.
"""
self.histdata_combined = pandas.DataFrame()
for symbol in self.stocklist:
self.histdata_indiv_stock = self.raw_all_stock_df[self.raw_all_stock_df['SYMBOL']== symbol]
self.add_Bollinger_parm()
self.add_MACD_parm()
self.add_pivot_point()
self.add_average_vol()
if len(self.histdata_combined) == 0:
self.histdata_combined = self.histdata_indiv_stock
else:
self.histdata_combined = self.histdata_combined.append(self.histdata_indiv_stock)
def bollinger_plots(self):
""" Plot the bollinger plots for each stocks.
Used self.histdata_indiv_stock for data passing.
Pivot is added to here
"""
self.histdata_indiv_stock.plot(x='Date', y=['Adj Close','20d_ma','50d_ma','Bol_upper','Bol_lower','Pivot','S1','R1' ])
self.histdata_indiv_stock.plot(x='Date', y=['Bol_BW','Bol_BW_200MA' ])
plt.show()
def MACD_plots(self):
""" Plot the MACD plots for each stocks.
Used self.histdata_indiv_stock for data passing.
"""
self.histdata_indiv_stock.plot(x='Date', y=['MACD','MACD_signalline' ],ylim=[-0.3,0.3])
#self.histdata_indiv_stock.plot(x='Date', y=['MACD_hist' ],kind='bar')#problem with this plot
plt.show()
def analysis_plot_for_tgt_sym(self, symbol):
""" Get all analysis plot for target symbol.
For each symbol, set to self.histdata_indiv_stock
Args:
symbol (str): stock symbol.
Type of plots to include
"""
self.histdata_indiv_stock = self.histdata_combined[self.histdata_combined['SYMBOL'] == symbol]
self.bollinger_plots()
self.MACD_plots()
def get_most_current_dataset(self):
""" Get the first (or most current) data for every stock.
Additional parm --> cross the bollinger band
cross the 50 days ex moving avg
Bol width - Bol 200 (percentage)
MACD hist (percentage)
"""
self.processed_histdata_combined = self.histdata_combined.groupby("SYMBOL").first().reset_index()
def add_response_trigger(self):
""" Add series of basic trigger point for basic technical analysis.
"""
tar_data = self.processed_histdata_combined
tar_data['Above_Boll_upper'] = tar_data['Adj Close'] > tar_data['Bol_upper']
tar_data['Below_Boll_lower'] = tar_data['Bol_lower'] > tar_data['Adj Close']
tar_data['price_above_50dexm'] = tar_data['Adj Close'] > tar_data['50d_exma']
tar_data['20dexm_above_50dexm'] = tar_data['20d_exma'] > tar_data['50d_exma']
self.processed_histdata_combined = tar_data
def display_avaliable_info_GUI(self):
""" For plot display in GUI.
"""
stock_choice = self.histdata_combined['SYMBOL'].drop_duplicates().tolist()
while True:
choice = easygui.choicebox(choices = stock_choice)
if choice == None:
return
else:
self.analysis_plot_for_tgt_sym(choice)
