minor changes, update candle appending to support overlaps

This commit is contained in:
Timothy Pogue 2022-12-05 13:11:07 -07:00
parent f1ebaf4730
commit 0602479f7d
3 changed files with 35 additions and 22 deletions

View File

@ -167,7 +167,8 @@ class DataProvider:
producer_name: str = "default"
) -> Tuple[bool, int]:
"""
Append a candle to the existing external dataframe
Append a candle to the existing external dataframe. The incoming dataframe
must have at least 1 candle.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
@ -176,29 +177,32 @@ class DataProvider:
"""
pair_key = (pair, timeframe, candle_type)
if (producer_name not in self.__producer_pairs_df) \
or (pair_key not in self.__producer_pairs_df[producer_name]):
if dataframe.empty:
# The incoming dataframe must have at least 1 candle
return (False, 0)
if (producer_name not in self.__producer_pairs_df
or pair_key not in self.__producer_pairs_df[producer_name]):
# We don't have data from this producer yet,
# or we don't have data for this pair_key
# return False and 1000 for the full df
return (False, 1000)
existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
existing_df, la = self.__producer_pairs_df[producer_name][pair_key]
# Iterate over any overlapping candles and update the values
for idx, candle in dataframe.iterrows():
existing_df.iloc[
existing_df['date'] == candle['date']
] = candle
existing_df.reset_index(drop=True, inplace=True)
# CHECK FOR MISSING CANDLES
timeframe_delta = to_timedelta(timeframe) # Convert the timeframe to a timedelta for pandas
local_last = existing_df.iloc[-1]['date'] # We want the last date from our copy of data
incoming_first = dataframe.iloc[0]['date'] # We want the first date from the incoming data
# We have received this candle before, update our copy
# and return True, 0
if local_last == incoming_first:
existing_df.iloc[-1] = dataframe.iloc[0]
existing_data = (existing_df.reset_index(drop=True), _)
self.__producer_pairs_df[producer_name][pair_key] = existing_data
return (True, 0)
candle_difference = (incoming_first - local_last) / timeframe_delta
# If the difference divided by the timeframe is 1, then this
@ -228,6 +232,7 @@ class DataProvider:
# Only keep the last 1500 candles in memory
existing = existing[-1500:] if len(existing) > 1500 else existing
existing.reset_index(drop=True, inplace=True)
return existing

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@ -36,6 +36,9 @@ class Producer(TypedDict):
ws_token: str
FULL_DATAFRAME_THRESHOLD = 100
logger = logging.getLogger(__name__)
@ -376,8 +379,8 @@ class ExternalMessageConsumer:
logger.debug(f"Received {len(df)} candle(s) for {key}")
if len(df) >= 999:
# This is a full dataframe
if len(df) >= FULL_DATAFRAME_THRESHOLD:
# This is likely a full dataframe
# Add the dataframe to the dataprovider
self._dp._add_external_df(
pair,
@ -388,8 +391,8 @@ class ExternalMessageConsumer:
producer_name=producer_name
)
elif len(df) < 999:
# This is n single candles
elif len(df) < FULL_DATAFRAME_THRESHOLD:
# This is likely n single candles
# Have dataprovider append it to
# the full datafame. If it can't,
# request the missing candles
@ -403,9 +406,14 @@ class ExternalMessageConsumer:
)
if not did_append:
logger.debug("Holes in data or no existing df, "
f"requesting {n_missing} candles "
f"for {key} from `{producer_name}`")
# We want an overlap in candles incase some data has changed
n_missing += 1
# Set to None for all candles if we missed a full df's worth of candles
n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500
logger.warning("Holes in data or no existing df, "
f"requesting {n_missing} candles "
f"for {key} from `{producer_name}`")
self.send_producer_request(
producer_name,

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@ -1062,7 +1062,7 @@ class RPC:
self,
pair: str,
timeframe: str,
limit: Optional[int] = None
limit: Optional[int]
) -> Tuple[DataFrame, datetime]:
"""
Get the dataframe and last analyze from the dataprovider
@ -1083,7 +1083,7 @@ class RPC:
def _ws_all_analysed_dataframes(
self,
pairlist: List[str],
limit: Optional[int] = None
limit: Optional[int]
) -> Generator[Dict[str, Any], None, None]:
"""
Get the analysed dataframes of each pair in the pairlist.