def create_symbol_lists():
sp500 = sym.get_sp500_symbols()
nyse = sym.get_nyse_symbols()
amex = sym.get_amex_symbols()
nasdaq = sym.get_nasdaq_symbols()
Df_sp500 = pd.DataFrame(sp500)
# Df_nyse = pd.DataFrame(nyse)
# Df_amex = pd.DataFrame(amex)
# Df_nasdaq = pd.DataFrame(nasdaq)
start = ""
with open('/root/jTWSdump_707/requests/base.txt', 'r') as f:
start = f.read()
# start += "\n"
f.close()
s_sp500 = ""
for symbol in Df_sp500.symbol:
if "^" in symbol or "." in symbol:
continue
#s_sp500 += '"'+symbol.strip()+'" "STK" "SMART" "" "" "" "USD" "" "10 D" "1 min" "1" "TRADES" "10" ""'+'\n'
s_sp500 += '"' + symbol.strip(
) + '" "STK" "SMART" "" "" "" "USD" "" "5 Y" "1 day" "1" "TRADES" "1" ""' + '\n'
s_nyse = ""
# for symbol in Df_nyse.symbol:
# if "^" in symbol or "." in symbol:
# continue
# # s_nyse += '"'+symbol.strip()+'" "STK" "SMART/NYSE" "" "" "" "USD" "" "10 D" "1 min" "1" "TRADES" "10" ""'+'\n'
# s_nyse += '"'+symbol.strip()+'" "STK" "SMART/NYSE" "" "" "" "USD" "" "5 Y" "1 day" "1" "TRADES" "1" ""'+'\n'
#
s_amex = ""
# for symbol in Df_amex.symbol:
# if "^" in symbol or "." in symbol:
# continue
# #s_amex += '"'+symbol.strip()+'" "STK" "SMART" "" "" "" "USD" "" "10 D" "1 min" "1" "TRADES" "10" ""'+'\n'
# s_amex += '"'+symbol.strip()+'" "STK" "SMART" "" "" "" "USD" "" "5 Y" "1 day" "1" "TRADES" "1" ""'+'\n'
#
s_nasdaq = ""
#
# for symbol in Df_nasdaq.symbol:
# if "^" in symbol or "." in symbol:
# continue
# #s_nasdaq += '"'+symbol.strip()+'" "STK" "SMART/NASDAQ" "" "" "" "USD" "" "10 D" "1 min" "1" "TRADES" "10" ""'+'\n'
# s_nasdaq += '"'+symbol.strip()+'" "STK" "SMART/NASDAQ" "" "" "" "USD" "" "5 Y" "1 day" "1" "TRADES" "1" ""'+'\n'
with open('/root/jTWSdump_707/requests/zipline.txt', 'w+') as f:
f.write(start)
f.write(s_sp500)
f.write(s_nyse)
f.write(s_amex)
f.write(s_nasdaq)
f.close()
def getSymbols(self):
idxs = self.cfg["general"]["idxs"].split(",")
symbols_list = []
sectors_map = {}
if "sp500" in idxs:
symbols_list += symbols.get_sp500_symbols()
if "nyse" in idxs:
symbols_list += symbols.get_nyse_symbols()
if "nasdaq" in idxs:
symbols_list += symbols.get_nasdaq_symbols()
if "amex" in idxs:
symbols_list += symbols.get_amex_symbols()
for s in symbols_list:
sectors_map[s["symbol"]] = s["sector"]
with open(self.pwd + "cfg/tickers.json", "w") as f:
json.dump(sectors_map, f)
# %% [markdown]
# # Load Financial Symbols
# %%
get_ipython().system('pip install finsymbols')
# %%
from finsymbols import symbols
import json
import pprint
#symbol_list = symbols.get_sp500_symbols()
#symbol_list.extend(symbols.get_amex_symbols())
#symbol_list.extend(symbols.get_nyse_symbols())
#symbol_list.extend(symbols.get_nasdaq_symbols())
symbol_list = symbols.get_nasdaq_symbols()
column_names = ['company', 'headquarters', 'industry', 'sector', 'symbol']
df = pd.DataFrame(symbol_list, columns=column_names)
my_symbols = df['symbol'].replace("\n", "", regex=True)
# %% [markdown]
# # Loops
# %% [markdown]
# ## Create expert Recordings
# %%
# Download List of NASDAQ Insturment
df = pd.read_csv('nasdaq_list.csv')
#df = df.iloc[17:]
df.head()
def test_nasdaq_not_null(self):
nasdaq = symbols.get_nasdaq_symbols()
assert len(nasdaq) != 0, 'NASDAQ list is of size 0'
def test_nasdaq_not_null(self):
nasdaq = symbols.get_nasdaq_symbols()
assert len(nasdaq) != 0, 'NASDAQ list is of size 0'
from pandas_datareader import data
from finsymbols import symbols
r"""
This script is used for downloading raw data of various stocks over the last ~15 years.
"""
all_stocks = symbols.get_nyse_symbols() + symbols.get_nasdaq_symbols()
for stock in all_stocks:
try:
symbol = stock['symbol']
historical_data = data.DataReader(symbol, 'Google', '2002-08-01', '2017-08-01')
if len(historical_data) > 252:
historical_data.to_csv('Raw_Stock_Data/' + symbol + '_data')
except:
pass