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https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-06 17:31:53 +00:00
Increase robustness of load command
This commit is contained in:
didier
@@ -181,41 +181,40 @@ def load(l_args, s_ticker, s_start, s_interval, df_stock):
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if not ns_parser:
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return [s_ticker, s_start, s_interval, df_stock]
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except SystemExit:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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# Update values:
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s_ticker = ns_parser.s_ticker.upper()
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s_start = ns_parser.s_start_date
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s_interval = str(ns_parser.n_interval) + "min"
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try:
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# Daily
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if s_interval == "1440min":
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if ns_parser.n_interval == 1440:
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# Alpha Vantage Source
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if ns_parser.source == "av":
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ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas")
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# pylint: disable=unbalanced-tuple-unpacking
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df_stock, _ = ts.get_daily_adjusted(
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df_stock_candidate, _ = ts.get_daily_adjusted(
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symbol=ns_parser.s_ticker, outputsize="full"
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)
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# pylint: disable=no-member
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df_stock.sort_index(ascending=True, inplace=True)
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# Slice dataframe from the starting date YYYY-MM-DD selected
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df_stock = df_stock[ns_parser.s_start_date :]
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elif ns_parser.source == "yf":
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df_stock = yf.download(
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ns_parser.s_ticker, start=ns_parser.s_start_date, progress=False
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)
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# Check that a stock was successfully retrieved
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if df_stock.empty:
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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df_stock = df_stock.rename(
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# pylint: disable=no-member
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df_stock_candidate.sort_index(ascending=True, inplace=True)
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# Slice dataframe from the starting date YYYY-MM-DD selected
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df_stock_candidate = df_stock_candidate[ns_parser.s_start_date :]
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# Yahoo Finance Source
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elif ns_parser.source == "yf":
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df_stock_candidate = yf.download(
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ns_parser.s_ticker, start=ns_parser.s_start_date, progress=False
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)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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df_stock_candidate = df_stock_candidate.rename(
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columns={
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"Open": "1. open",
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"High": "2. high",
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@@ -225,41 +224,57 @@ def load(l_args, s_ticker, s_start, s_interval, df_stock):
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"Volume": "6. volume",
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}
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)
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df_stock.index.name = "date"
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df_stock_candidate.index.name = "date"
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if df_stock.index[0] > s_start:
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s_start = df_stock.index[0]
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# Check if start time from dataframe is more recent than specified
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if df_stock_candidate.index[0] > pd.to_datetime(ns_parser.s_start_date):
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s_start = df_stock_candidate.index[0]
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else:
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s_start = ns_parser.s_start_date
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# Intraday
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else:
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# Alpha Vantage Source
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if ns_parser.source == "av":
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ts = TimeSeries(key=cfg.API_KEY_ALPHAVANTAGE, output_format="pandas")
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# pylint: disable=unbalanced-tuple-unpacking
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df_stock, _ = ts.get_intraday(
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symbol=ns_parser.s_ticker, outputsize="full", interval=s_interval
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df_stock_candidate, _ = ts.get_intraday(
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symbol=ns_parser.s_ticker,
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outputsize="full",
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interval=str(ns_parser.n_interval) + "min",
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)
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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# pylint: disable=no-member
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df_stock.sort_index(ascending=True, inplace=True)
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df_stock_candidate.sort_index(ascending=True, inplace=True)
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# Slice dataframe from the starting date YYYY-MM-DD selected
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df_stock = df_stock[ns_parser.s_start_date :]
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df_stock_candidate = df_stock_candidate[ns_parser.s_start_date :]
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# Check if start time from dataframe is more recent than specified
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if df_stock_candidate.index[0] > pd.to_datetime(ns_parser.s_start_date):
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s_start = df_stock_candidate.index[0]
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else:
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s_start = ns_parser.s_start_date
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# Yahoo Finance Source
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elif ns_parser.source == "yf":
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s_int = s_interval[:-2]
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s_int = str(ns_parser.n_interval) + "m"
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d_granularity = {"1m": 6, "5m": 59, "15m": 59, "30m": 59, "60m": 729}
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s_start_dt = datetime.utcnow() - timedelta(days=d_granularity[s_int])
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s_date_start = s_start_dt.strftime("%Y-%m-%d")
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s_start = pytz.utc.localize(s_start_dt)
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if s_start_dt > ns_parser.s_start_date:
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print(
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f"Using Yahoo Finance with granularity {s_int} the starting date is set to: {s_date_start}\n"
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)
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# Using Yahoo Finance with granularity {s_int} the starting date is set to: {s_date_start}
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df_stock = yf.download(
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df_stock_candidate = yf.download(
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ns_parser.s_ticker,
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start=s_date_start,
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progress=False,
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@@ -268,7 +283,7 @@ def load(l_args, s_ticker, s_start, s_interval, df_stock):
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)
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else:
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df_stock = yf.download(
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df_stock_candidate = yf.download(
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ns_parser.s_ticker,
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start=ns_parser.s_start_date.strftime("%Y-%m-%d"),
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progress=False,
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@@ -276,12 +291,17 @@ def load(l_args, s_ticker, s_start, s_interval, df_stock):
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prepost=ns_parser.b_prepost,
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)
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# Check that a stock was successfully retrieved
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if df_stock.empty:
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# Check that loading a stock was not successful
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if df_stock_candidate.empty:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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df_stock = df_stock.rename(
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if s_start_dt > ns_parser.s_start_date:
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s_start = pytz.utc.localize(s_start_dt)
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else:
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s_start = ns_parser.s_start_date
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df_stock_candidate = df_stock_candidate.rename(
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columns={
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"Open": "1. open",
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"High": "2. high",
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@@ -291,26 +311,28 @@ def load(l_args, s_ticker, s_start, s_interval, df_stock):
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"Volume": "6. volume",
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}
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)
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df_stock.index.name = "date"
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df_stock_candidate.index.name = "date"
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return [s_ticker, s_start, s_interval, df_stock]
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s_intraday = (f"Intraday {s_interval}", "Daily")[ns_parser.n_interval == 1440]
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print(
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f"Loading {s_intraday} {ns_parser.s_ticker.upper()} stock with starting period {s_start.strftime('%Y-%m-%d')} for analysis.\n"
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)
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return [
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ns_parser.s_ticker.upper(),
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s_start,
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str(ns_parser.n_interval) + "min",
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df_stock_candidate,
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]
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except Exception as e:
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print(e)
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print("Either the ticker or the API_KEY are invalids. Try again!\n")
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print(e, "\nEither the ticker or the API_KEY are invalids. Try again!\n")
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return [s_ticker, s_start, s_interval, df_stock]
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s_intraday = (f"Intraday {s_interval}", "Daily")[s_interval == "1440min"]
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if s_start:
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print(
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f"Loading {s_intraday} {s_ticker} stock with starting period {s_start.strftime('%Y-%m-%d')} for analysis."
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)
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else:
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print(f"Loading {s_intraday} {s_ticker} stock for analysis.")
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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except SystemExit:
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print("")
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return [s_ticker, s_start, s_interval, df_stock]
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def candle(s_ticker: str, s_start: str):
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@@ -47,7 +47,7 @@ def main():
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os.system("")
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s_ticker = ""
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s_start = ""
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s_start = "2015-01-01"
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df_stock = pd.DataFrame()
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s_interval = "1440min"
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