def test_kline(stock_id, start_date, end_date):
dl = DataLoader()
stock_data = dl.taiwan_stock_daily_adj(stock_id=stock_id,
start_date=start_date,
end_date=end_date)
assert plotting.kline(stock_data)
stock_data = dl.feature.add_kline_institutional_investors(stock_data)
assert plotting.kline(stock_data)
stock_data = dl.feature.add_kline_margin_purchase_short_sale(stock_data)
assert plotting.kline(stock_data)
def df():
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
df = data_loader.taiwan_stock_holding_shares_per(stock_id="2890",
start_date="2021-06-01",
end_date="2021-07-03")
df["labels"] = df["HoldingSharesLevel"]
df["series"] = df["percent"]
return df
def test_bar_success(stock_id, start_date, end_date):
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
df = data_loader.taiwan_stock_month_revenue(stock_id=stock_id,
start_date=start_date,
end_date=end_date)
df["labels"] = (df[["revenue_year", "revenue_month"]].astype(str).apply(
lambda date: f"{date[0]}-{date[1]}M", axis=1))
df["series"] = df["revenue"].map(lambda value: round(value * 1e-8, 2))
assert plotting.bar(labels=df["labels"], series=df["series"])
def df():
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
df = data_loader.taiwan_stock_month_revenue(stock_id="2890",
start_date="2018-1M",
end_date="2021-7M")
df["labels"] = (df[["revenue_year", "revenue_month"]].astype(str).apply(
lambda date: f"{date[0]}-{date[1]}M", axis=1))
df["series"] = df["revenue"].map(lambda value: round(value * 1e-8, 2))
return df
def test_pie_failed():
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
df = data_loader.taiwan_stock_month_revenue(stock_id="2330",
start_date="2018-01-01",
end_date="2021-03-03")
df["series"] = (df[["revenue_year", "revenue_month"]].astype(str).apply(
lambda date: f"{date[0]}-{date[1]}M", axis=1))
df["labels"] = df["revenue"].map(lambda value: round(value * 1e-8, 2))
with (pytest.raises(Exception)):
plotting.pie(labels=df["labels"], series=df["series"])
def test_pie(stock_id, start_date, end_date):
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
df = data_loader.taiwan_stock_holding_shares_per(stock_id=stock_id,
start_date=start_date,
end_date=end_date)
df = df[df["date"] == max(df["date"])]
df = df[df["HoldingSharesLevel"] != "total"]
df["labels"] = df["HoldingSharesLevel"]
df["series"] = df["percent"]
assert plotting.pie(labels=df["labels"], series=df["series"])
def test_kline(stock_id, start_date, end_date):
user_id = os.environ.get("FINMIND_USER", "")
password = os.environ.get("FINMIND_PASSWORD", "")
data_loader = DataLoader()
data_loader.login(user_id, password)
stock_data = data_loader.taiwan_stock_daily_adj(stock_id=stock_id,
start_date=start_date,
end_date=end_date)
assert plotting.kline(stock_data)
stock_data = data_loader.feature.add_kline_institutional_investors(
stock_data)
assert plotting.kline(stock_data)
stock_data = data_loader.feature.add_kline_margin_purchase_short_sale(
stock_data)
assert plotting.kline(stock_data)
def data_loader():
data_loader = DataLoader()
data_loader.login(user_id, password)
return data_loader
def get_asset_underlying_type(stock_id: str, data_loader: DataLoader) -> str:
taiwan_stock_info = data_loader.taiwan_stock_info()
underlying_type = taiwan_stock_info[
taiwan_stock_info["stock_id"] ==
stock_id]["industry_category"].values[0]
return underlying_type
