def insider(l_args, s_ticker):
parser = argparse.ArgumentParser(
prog='insider',
description=
"""Prints information about inside traders. The following fields are expected:
Date, Relationship, Transaction, #Shares, Cost, Value ($), #Shares Total, Insider Trading,
SEC Form 4. [Source: Finviz]""")
parser.add_argument('-n',
"--num",
action="store",
dest="n_num",
type=check_positive,
default=10,
help='number of latest inside traders.')
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
if l_unknown_args:
print(
f"The following args couldn't be interpreted: {l_unknown_args}\n")
return
d_finviz_insider = finviz.get_insider(s_ticker)
df_fa = pd.DataFrame.from_dict(d_finviz_insider)
df_fa.set_index("Date", inplace=True)
df_fa = df_fa[[
'Relationship', 'Transaction', '#Shares', 'Cost', 'Value ($)',
'#Shares Total', 'Insider Trading', 'SEC Form 4'
]]
print(df_fa.head(n=ns_parser.n_num))
print("")
def get_insider(ticker):
try:
insider = finviz.get_insider(ticker)
insider = pd.DataFrame(insider)
insider = insider.set_index('Date')
return insider
except Exception as e:
return e
def insider(other_args: List[str], ticker: str):
"""Display insider activity for a given stock ticker
Parameters
----------
other_args : List[str]
argparse other args - ["-n", "10"]
ticker : str
Stock ticker
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="insider",
description="""
Prints information about inside traders. The following fields are expected: Date, Relationship,
Transaction, #Shares, Cost, Value ($), #Shares Total, Insider Trading, SEC Form 4. [Source: Finviz]
""",
)
parser.add_argument(
"-n",
"--num",
action="store",
dest="n_num",
type=check_positive,
default=10,
help="number of latest inside traders.",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
d_finviz_insider = finviz.get_insider(ticker)
df_fa = pd.DataFrame.from_dict(d_finviz_insider)
df_fa.set_index("Date", inplace=True)
df_fa = df_fa[[
"Relationship",
"Transaction",
"#Shares",
"Cost",
"Value ($)",
"#Shares Total",
"Insider Trading",
"SEC Form 4",
]]
print(df_fa.head(n=ns_parser.n_num))
print("")
except Exception as e:
print(e)
print("")
return
def get_last_insider_activity(ticker: str) -> Dict:
"""Get last insider activity for a given stock ticker. [Source: Finviz]
Parameters
----------
ticker : str
Stock ticker
Dict
Latest insider trading activity
"""
return finviz.get_insider(ticker)
def insider(l_args, s_ticker):
parser = argparse.ArgumentParser(
prog="insider",
description="""
Prints information about inside traders. The following fields are expected: Date, Relationship,
Transaction, #Shares, Cost, Value ($), #Shares Total, Insider Trading, SEC Form 4. [Source: Finviz]
""",
)
parser.add_argument(
"-n",
"--num",
action="store",
dest="n_num",
type=check_positive,
default=10,
help="number of latest inside traders.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
d_finviz_insider = finviz.get_insider(s_ticker)
df_fa = pd.DataFrame.from_dict(d_finviz_insider)
df_fa.set_index("Date", inplace=True)
df_fa = df_fa[[
"Relationship",
"Transaction",
"#Shares",
"Cost",
"Value ($)",
"#Shares Total",
"Insider Trading",
"SEC Form 4",
]]
print(df_fa.head(n=ns_parser.n_num))
print("")
except Exception as e:
print(e)
print("")
return
def data_layer(sp_100_file, data_path, perc_space, perc_time):
sp_100_df = pd.read_csv(sp_100_file)
simbols = sp_100_df.Symbol.values[:-1]
map_month2month_number = dict(
(v, k) for k, v in enumerate(calendar.month_abbr))
stock = {}
cont = 0
for simbol in simbols:
try:
insider_info = fz.get_insider(simbol)
insider_data = pd.DataFrame.from_dict(insider_info)
insider_data[["month_name",
"day"]] = insider_data.Date.str.split(" ",
expand=True)
insider_data["month"] = insider_data["month_name"].map(
map_month2month_number)
insider_data["year"] = insider_data["month"].apply(
infer_year(datetime.datetime.now().month,
datetime.datetime.now().year))
insider_data["date"] = pd.to_datetime(
insider_data[["year", "month", "day"]], format="%y%m%d")
cont = cont + 1
except Exception as inst:
print(simbol, end=" ")
print(type(inst), end=" ") # the exception instance
print(inst)
continue
curr_ticker = yf.Ticker(simbol)
curr_hist = curr_ticker.history(period="1y")
curr_table = pd.pivot_table(insider_data,
index=['date'],
columns=['Transaction'],
aggfunc={"Transaction": len})
curr_hist["volatility"] = (curr_hist.High - curr_hist.Low) / (
curr_hist.Open + np.finfo(float).eps)
curr_hist["change_price_beg_end"] = curr_hist.Open - curr_hist.Close
curr_hist["change_price"] = curr_hist.High - curr_hist.Low
curr_hist[
"Volume_rel_price"] = curr_hist.Volume * curr_hist["change_price"]
curr_hist[
"Vol_rel_volatility"] = curr_hist.Volume * curr_hist["volatility"]
curr_table = ~curr_table.isnull()
curr_table.columns = [col[1] for col in curr_table.columns]
curr_hist = curr_hist.merge(curr_table,
how="left",
left_index=True,
right_index=True)
curr_hist = curr_hist.fillna(False)
if "Buy" not in curr_hist.columns:
curr_hist["Buy"] = False
if "Sale" not in curr_hist.columns:
curr_hist["Sale"] = False
if "Option Exercise" not in curr_hist.columns:
curr_hist["Option Exercise"] = False
stock[simbol] = curr_hist
n_train_space = int(len(stock.keys()) * perc_space)
n_train_out_space = len(stock.keys()) - n_train_space
train_space = np.random.choice(list(stock.keys()),
size=n_train_space,
replace=False).tolist()
test_space = list(set(stock.keys()) - set(train_space))
return stock