GroupedCandleUniverse#

tradingstrategy.candle.GroupedCandleUniverse class.

class GroupedCandleUniverse[source]#

Bases: PairGroupedUniverse

A candle universe where each trading pair has its own candles.

This is helper class to create foundation for multi pair strategies.

For the data logistics purposes, all candles are lumped together in single columnar data blobs. However, it rarely makes sense to execute operations over different trading pairs. :py:class`GroupedCandleUniverse` creates trading pair id -> candle data grouping out from raw candle data.

Usage:

# Get candles for SUSHI-USDT

exchange_universe = client.fetch_exchange_universe()
raw_pairs = client.fetch_pair_universe().to_pandas()
raw_candles = client.fetch_all_candles(TimeBucket.d7).to_pandas()

pair_universe = PandasPairUniverse(raw_pairs)
candle_universe = GroupedCandleUniverse(raw_candles)

# Do some test calculations for a single pair
sushi_swap = exchange_universe.get_by_chain_and_name(ChainId.ethereum, "sushi")
sushi_usdt = pair_universe.get_one_pair_from_pandas_universe(sushi_swap.exchange_id, "SUSHI", "USDT")

raw_candles = client.fetch_all_candles(TimeBucket.d7).to_pandas()
candle_universe = GroupedCandleUniverse(raw_candles)
sushi_usdth_candles = candle_universe.get_candles_by_pair(sushi_usdt.pair_id)

__init__(df, time_bucket=TimeBucket.d1, timestamp_column='timestamp', index_automatically=True)#

Parameters

time_bucket – What bar size candles we are operating at. Default to daily. TODO: Deprecate - not used?
timestamp_column – What column use to build a time index. Used for QStrader / Backtrader compatibility.
index_automatically – Convert the index to use time series. You might avoid this with QSTrader kind of data.
df (DataFrame) –

Methods

`__init__`(df[, time_bucket, ...])	param time_bucket
`create_empty`()	Return an empty GroupedCandleUniverse
`create_empty_qstrader`()	Return an empty GroupedCandleUniverse.
`create_from_single_pair_dataframe`(df)	Construct universe based on a single trading pair data.
`get_all_pairs`()	Go through all liquidity samples, one DataFrame per trading pair.
`get_all_samples_by_range`(start, end)	Get list of candles/samples for all pairs at a certain range.
`get_all_samples_by_timestamp`(ts)	Get list of candles/samples for all pairs at a certain timepoint.
`get_candle_count`()	Return the dataset size - how many candles total
`get_candles_by_pair`(pair_id)	Get candles for a single pair.
`get_closest_price`(pair_id, when[, kind, ...])	Get the available liuqidity for a trading pair at a specific timepoint or some candles before the timepoint.
`get_columns`()	Get column names from the underlying pandas.GroupBy object
`get_pair_count`()	Return the number of pairs in this dataset
`get_pair_ids`()	Get all pairs present in the dataset
`get_prior_timestamp`(ts)	Get the first timestamp in the index that is before the given timestamp.
`get_sample_count`()	Return the dataset size - how many samples total for all pairs
`get_samples_by_pair`(pair_id)	Get samples for a single pair.
`get_single_pair_data`([timestamp, sample_count])	Get all candles/liquidity samples for the single alone pair in the universe.
`get_timestamp_range`([use_timezone])	Return the time range of data we have for.
`iterate_samples_by_pair_range`(start, end)	Get list of candles/samples for all pairs at a certain range.

get_candle_count()[source]#

Return the dataset size - how many candles total

Return type: int

get_candles_by_pair(pair_id)[source]#

Get candles for a single pair.

Returns

Pandas dataframe object with the following columns

timestamp
open
high
low
close

Parameters

pair_id (PrimaryKey) –

Return type

Optional[DataFrame]

get_closest_price(pair_id, when, kind='close', look_back_time_frames=5)[source]#

Get the available liuqidity for a trading pair at a specific timepoint or some candles before the timepoint.

The liquidity is defined as one-sided as in XY liquidity model.

Parameters

pair_id (PrimaryKey) – Trading pair id
when (Timestamp) – Timestamp to query
kind – One of OHLC data points: “open”, “close”, “low”, “high”
look_back_timeframes – If there is no liquidity sample available at the exact timepoint, look to the past to the get the nearest sample. For example if candle time interval is 5 minutes and look_back_timeframes is 10, then accept a candle that is maximum of 50 minutes before the timepoint.

Returns