def qtr_contracts_hist(other_args: List[str], ticker: str):
"""Quarter contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get congress trading data from
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="qtr_contracts_hist",
description=
"Quarterly-contracts historical [Source: www.quiverquant.com]",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading(
"quarter-contracts", ticker=ticker)
if df_contracts.empty:
print("No quarterly government contracts found\n")
return
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
amounts = df_contracts.sort_values(by=["Year", "Qtr"])["Amount"].values
qtr = df_contracts.sort_values(by=["Year", "Qtr"])["Qtr"].values
year = df_contracts.sort_values(by=["Year", "Qtr"])["Year"].values
quarter_ticks = [
f"{quarter[0]}" if quarter[1] == 1 else ""
for quarter in zip(year, qtr)
]
plt.plot(np.arange(0, len(amounts)), amounts / 1000, "-*", lw=2, ms=15)
plt.xlim([-0.5, len(amounts) - 0.5])
plt.xticks(np.arange(0, len(amounts)), quarter_ticks)
plt.grid()
plt.title(
f"Quarterly Government Contracts Historical on {ticker.upper()}")
plt.xlabel("Date")
plt.ylabel("Amount [1k $]")
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def raw_contracts(other_args: List[str], ticker: str):
"""Raw contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get congress trading data from
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="raw_contracts",
description="Raw contracts. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_days",
action="store",
dest="past_transactions_days",
type=check_positive,
default=10,
help="Past transaction days",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading(
"contracts", ticker)
if df_contracts.empty:
print("No government contracts found\n")
return
df_contracts["Date"] = pd.to_datetime(df_contracts["Date"]).dt.date
df_contracts.drop_duplicates(inplace=True)
df_contracts = df_contracts[df_contracts["Date"].isin(
df_contracts["Date"].unique()[:ns_parser.past_transactions_days])]
df_contracts.drop_duplicates(inplace=True)
print(df_contracts.to_string(index=False))
print("")
except Exception as e:
print(e, "\n")
def lobbying(other_args: List[str], ticker: str):
"""Corporate lobbying details
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get corporate lobbying data from
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="lobbying",
description="Lobbying details [Source: www.quiverquant.com]",
)
parser.add_argument(
"-l",
"--last",
action="store",
dest="last",
type=check_positive,
default=10,
help="Last corporate lobbying details",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_lobbying = quiverquant_model.get_government_trading(
"corporate-lobbying", ticker=ticker)
if df_lobbying.empty:
print("No corporate lobbying found\n")
return
for _, row in (df_lobbying.sort_values(
by=["Date"], ascending=False).head(ns_parser.last).iterrows()):
amount = ("$" + str(int(float(row["Amount"])))
if row["Amount"] is not None else "N/A")
print(f"{row['Date']}: {row['Client']} {amount}")
if row["Amount"] is not None:
print(
"\t" +
row["Specific_Issue"].replace("\n", " ").replace("\r", ""))
print("")
print("")
except Exception as e:
print(e, "\n")
def sum_contracts(other_args: List[str]):
"""Sum contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="sum_contracts",
description="Sum latest contracts. [Source: www.quiverquant.com]",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading("contracts")
if df_contracts.empty:
print("No government contracts found\n")
return
df_contracts["Date"] = pd.to_datetime(df_contracts["Date"]).dt.date
df_contracts.drop_duplicates(inplace=True)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_contracts.groupby("Date").sum().div(1000).plot(kind="bar",
rot=0,
ax=plt.gca())
plt.ylabel("Amount [1k $]")
plt.title("Sum of latest government contracts")
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def top_lobbying(other_args: List[str]):
"""Top lobbying based on tickers that have biggest amounts for the past couple months
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="top_lobbying",
description="Top lobbying. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-t",
"--top",
action="store",
dest="top",
type=check_positive,
default=10,
help="Top corporate lobbying tickers with biggest amounts",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_lobbying = quiverquant_model.get_government_trading(
"corporate-lobbying")
if df_lobbying.empty:
print("No corporate lobbying found\n")
return
d_lobbying = {}
for symbol in df_lobbying["Ticker"].unique():
d_lobbying[symbol] = sum(
float(amount)
for amount in df_lobbying[df_lobbying["Ticker"] == symbol].
replace(np.nan, 0)["Amount"].values)
df_amount = pd.DataFrame.from_dict(d_lobbying,
orient="index",
columns=["Amount"]).sort_values(
by=["Amount"], ascending=False)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
plt.bar(
df_amount.head(ns_parser.top).index,
df_amount.head(ns_parser.top).values.flatten() / 1000,
)
plt.xlabel("Ticker")
plt.ylabel("Sum Amount [1k $]")
plt.title(
f"Total amount spent on corporate lobbying since {df_lobbying['Date'].min()}"
)
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def qtr_contracts(other_args: List[str]):
"""Quarter contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="qtr_contracts",
description=
"Quarterly-contracts, best regression slope. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-t",
"--top",
action="store",
dest="top",
type=check_positive,
default=5,
help="Top promising stocks with best quarterly-contracts momentum",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading(
"quarter-contracts")
if df_contracts.empty:
print("No quarterly government contracts found\n")
return
df_coef = pd.DataFrame(columns=["Ticker", "Coef"])
for symbol in df_contracts["Ticker"].unique():
# Create linear regression object
regr = linear_model.LinearRegression()
amounts = (
df_contracts[df_contracts["Ticker"] == symbol].sort_values(
by=["Year", "Qtr"])["Amount"].values)
# Train the model using the training sets
regr.fit(np.arange(0, len(amounts)).reshape(-1, 1), amounts)
df_coef = df_coef.append({
"Ticker": symbol,
"Coef": regr.coef_[0]
},
ignore_index=True)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
tickers = df_coef.sort_values(by=["Coef"], ascending=False).head(
ns_parser.top)["Ticker"]
max_amount = 0
quarter_ticks = list()
for symbol in tickers:
amounts = (
df_contracts[df_contracts["Ticker"] == symbol].sort_values(
by=["Year", "Qtr"])["Amount"].values)
qtr = (df_contracts[df_contracts["Ticker"] == symbol].sort_values(
by=["Year", "Qtr"])["Qtr"].values)
year = (df_contracts[df_contracts["Ticker"] == symbol].sort_values(
by=["Year", "Qtr"])["Year"].values)
plt.plot(np.arange(0, len(amounts)),
amounts / 1000,
"-*",
lw=2,
ms=15)
if len(amounts) > max_amount:
max_amount = len(amounts)
quarter_ticks = [
f"{quarter[0]} - {quarter[1]} Qtr"
for quarter in zip(year, qtr)
]
plt.xlim([-0.5, max_amount - 0.5])
plt.xticks(np.arange(0, max_amount), quarter_ticks)
plt.grid()
plt.legend(tickers)
plt.title("Quarterly Government Contracts - Top promising stocks")
plt.xlabel("Date")
plt.ylabel("Amount [1k $]")
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def contracts(other_args: List[str], ticker: str):
"""Contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get congress trading data from
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="contracts",
description=
"Contracts associated with ticker. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_days",
action="store",
dest="past_transactions_days",
type=check_positive,
default=10,
help="Past transaction days",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading(
"contracts", ticker)
if df_contracts.empty:
print("No government contracts found\n")
return
df_contracts["Date"] = pd.to_datetime(df_contracts["Date"]).dt.date
df_contracts = df_contracts[df_contracts["Date"].isin(
df_contracts["Date"].unique()[:ns_parser.past_transactions_days])]
df_contracts.drop_duplicates(inplace=True)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_contracts.groupby("Date").sum().div(1000).plot(kind="bar",
rot=0,
ax=plt.gca())
plt.ylabel("Amount [1k $]")
plt.title(f"Sum of latest government contracts to {ticker}")
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def last_contracts(other_args: List[str]):
"""Last contracts
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="last_contracts",
description="Last contracts. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_days",
action="store",
dest="past_transactions_days",
type=check_positive,
default=2,
help="Past transaction days",
)
parser.add_argument(
"-l",
"--limit",
action="store",
dest="limit_contracts",
type=check_positive,
default=20,
help="Limit of contracts to display",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_contracts = quiverquant_model.get_government_trading("contracts")
if df_contracts.empty:
print("No government contracts found\n")
return
df_contracts.sort_values("Date", ascending=False)
df_contracts["Date"] = pd.to_datetime(df_contracts["Date"])
df_contracts.drop_duplicates(inplace=True)
df_contracts = df_contracts[df_contracts["Date"].isin(
df_contracts["Date"].unique()[:ns_parser.past_transactions_days])]
df_contracts = df_contracts[[
"Date", "Ticker", "Amount", "Description", "Agency"
]][:ns_parser.limit_contracts]
print(df_contracts.to_string(index=False))
print("")
except Exception as e:
print(e, "\n")
def raw_government(other_args: List[str], ticker: str, gov_type: str):
"""Raw government trading
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get congress trading data from
gov_type: str
Type of government data between: congress, senate and house
"""
parser = argparse.ArgumentParser(
add_help=False,
prog=gov_type,
description=f"Raw {gov_type} trading. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_days",
action="store",
dest="past_transactions_days",
type=check_positive,
default=10,
help="Past transaction days",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_gov = quiverquant_model.get_government_trading(gov_type, ticker)
if df_gov.empty:
print(f"No {gov_type} trading data found\n")
return
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
if gov_type == "congress":
df_gov = df_gov[[
"TransactionDate", "Representative", "House", "Transaction",
"Range"
]]
else:
df_gov = df_gov[[
"TransactionDate", "Representative", "Transaction", "Range"
]]
df_gov = df_gov[df_gov["TransactionDate"].isin(
df_gov["TransactionDate"].unique()
[:ns_parser.past_transactions_days])].rename(
columns={
"TransactionDate": "Transaction Date",
})
print(df_gov.to_string(index=False))
print("")
except Exception as e:
print(e, "\n")
def government_trading(other_args: List[str], ticker: str, gov_type: str):
"""Government trading
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
ticker: str
Ticker to get congress trading data from
gov_type: str
Type of government data between: congress, senate and house
"""
parser = argparse.ArgumentParser(
add_help=False,
prog=gov_type,
description=f"{gov_type} trading. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_months",
action="store",
dest="past_transactions_months",
type=check_positive,
default=6,
help="Past transaction months",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_gov = quiverquant_model.get_government_trading(gov_type, ticker)
if df_gov.empty:
print(f"No {gov_type} trading data found\n")
return
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
start_date = datetime.now() - timedelta(
days=ns_parser.past_transactions_months * 30)
df_gov["TransactionDate"] = pd.to_datetime(df_gov["TransactionDate"])
df_gov = df_gov[df_gov["TransactionDate"] > start_date]
if df_gov.empty:
print(f"No recent {gov_type} trading data found\n")
return
df_gov["min"] = df_gov["Range"].apply(
lambda x: x.split("-")[0].strip("$").replace(",", "").strip())
df_gov["max"] = df_gov["Range"].apply(
lambda x: x.split("-")[1].replace(",", "").strip().strip("$")
if "-" in x else x.strip("$").replace(",", ""))
df_gov["lower"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: int(x["min"])
if x["Transaction"] == "Purchase" else -int(x["max"]),
axis=1,
)
df_gov["upper"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: int(x["max"])
if x["Transaction"] == "Purchase" else -int(x["min"]),
axis=1,
)
df_gov = df_gov.sort_values("TransactionDate", ascending=True)
plot_government(df_gov, ticker, gov_type)
print("")
except Exception as e:
print(e, "\n")
def sell_government(other_args: List[str], gov_type: str):
"""Top sell government trading
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
gov_type: str
Type of government data between: congress, senate and house
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="sell_" + gov_type,
description=
f"Top sell {gov_type} trading. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_months",
action="store",
dest="past_transactions_months",
type=check_positive,
default=6,
help="Past transaction months",
)
parser.add_argument(
"-t",
"--top",
action="store",
dest="top_num",
type=check_positive,
default=10,
help="Number of top tickers",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_gov = quiverquant_model.get_government_trading(gov_type)
if df_gov.empty:
print(f"No {gov_type} trading data found\n")
return
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
start_date = datetime.now() - timedelta(
days=ns_parser.past_transactions_months * 30)
df_gov["TransactionDate"] = pd.to_datetime(df_gov["TransactionDate"])
df_gov = df_gov[df_gov["TransactionDate"] > start_date].dropna()
df_gov["min"] = df_gov["Range"].apply(
lambda x: x.split("-")[0].strip("$").replace(",", "").strip())
df_gov["max"] = df_gov["Range"].apply(
lambda x: x.split("-")[1].replace(",", "").strip().strip("$")
if "-" in x else x.strip("$").replace(",", ""))
df_gov["lower"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: float(x["min"])
if x["Transaction"] == "Purchase" else -float(x["max"]),
axis=1,
)
df_gov["upper"] = df_gov[["min", "max", "Transaction"]].apply(
lambda x: float(x["max"])
if x["Transaction"] == "Purchase" else -float(x["min"]),
axis=1,
)
df_gov = df_gov.sort_values("TransactionDate", ascending=True)
plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
df_gov.groupby("Ticker")["lower"].sum().div(
1000).sort_values().abs().head(n=ns_parser.top_num).plot(
kind="bar", rot=0)
plt.ylabel("Amount [1k $]")
plt.title(
f"Top {ns_parser.top_num} most sold stocks since last {ns_parser.past_transactions_months} months"
" (upper bound)")
plt.gcf().axes[0].yaxis.get_major_formatter().set_scientific(False)
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e, "\n")
def last_government(other_args: List[str], gov_type: str):
"""Last government trading
Parameters
----------
other_args : List[str]
Command line arguments to be processed with argparse
gov_type: str
Type of government data between: congress, senate and house
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="last_" + gov_type,
description=f"Last {gov_type} trading. [Source: www.quiverquant.com]",
)
parser.add_argument(
"-p",
"--past_transactions_days",
action="store",
dest="past_transactions_days",
type=check_positive,
default=5,
help="Past transaction days",
)
parser.add_argument(
"-r",
"--representative",
action="store",
dest="representative",
type=str,
default="",
help="Representative",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-p")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
df_gov = quiverquant_model.get_government_trading(gov_type)
if df_gov.empty:
print(f"No {gov_type} trading data found\n")
return
df_gov = df_gov.sort_values("TransactionDate", ascending=False)
df_gov = df_gov[df_gov["TransactionDate"].isin(
df_gov["TransactionDate"].unique()
[:ns_parser.past_transactions_days])]
if gov_type == "congress":
df_gov = df_gov[[
"TransactionDate",
"Ticker",
"Representative",
"Transaction",
"Range",
"House",
"ReportDate",
]].rename(
columns={
"TransactionDate": "Transaction Date",
"ReportDate": "Report Date",
})
else:
df_gov = df_gov[[
"TransactionDate",
"Ticker",
"Representative",
"Transaction",
"Range",
]].rename(columns={"TransactionDate": "Transaction Date"})
if ns_parser.representative:
df_gov_rep = df_gov[df_gov["Representative"].str.split().str[0] ==
ns_parser.representative]
if df_gov_rep.empty:
print(
f"No representative {ns_parser.representative} found in the past {ns_parser.past_transactions_days}"
f" days. The following are available: "
f"{', '.join(df_gov['Representative'].str.split().str[0].unique())}"
)
else:
print(df_gov_rep.to_string(index=False))
else:
print(df_gov.to_string(index=False))
print("")
except Exception as e:
print(e, "\n")
