Liquidity

Liquidity data feed manipulation.

For more information about liquidity in automatic market making pools see AMM.

class tradingstrategy.liquidity.XYLiquidity

Data structure that presents liquidity status in bonding curve pool.

This data structure is for naive x*y=k AMM pool. Liquidity is not the part of the normal technical analysis, so the dataset server has separate datasets for it.

Liquidity is expressed as US dollar value of the quote token of the pool. For example if the pool is 50 $FOO in reserve0 and 50 $USDC in reserve1, the liquidity of the pool would be expressed as 50 USD.

Liquidity events, like candles, have open, high, low and close values, depending on which time of the candle they were sampled.

pair_id: tradingstrategy.types.PrimaryKey

Primary key to identity the trading pair Use pair universe to map this to chain id and a smart contract address

timestamp: tradingstrategy.types.UNIXTimestamp

Open timestamp for this time bucket.

exchange_rate: float

USD exchange rate of the quote token used to convert to dollar amounts in this time bucket.

Note that currently any USD stablecoin (USDC, DAI) is assumed to be 1:1 and the candle server cannot handle exchange rate difference among stablecoins.

The rate is taken at the beginning of the 1 minute time bucket. For other time buckets, the exchange rate is the simple average for the duration of the bucket.

open: tradingstrategy.types.USDollarAmount

Liquidity absolute values in the pool in different time points. Note - for minute candles - if the candle contains only one event (mint, burn, sync) the open liquidity value is the value AFTER this event. The dataset server does not track the closing value of the previous liquidity event. This applies for minute candles only.

close: tradingstrategy.types.USDollarAmount

Liquidity absolute values in the pool in different time points

high: tradingstrategy.types.USDollarAmount

Liquidity absolute values in the pool in different time points

low: tradingstrategy.types.USDollarAmount

Liquidity absolute values in the pool in different time points

adds: int

Number of liquidity supplied events for pool

removes: int

Number of liquidity removed events for the pool

syncs: int

Number of total events affecting liquidity during the time window. This is adds, removes AND swaps AND sync().

add_volume: tradingstrategy.types.USDollarAmount

How much new liquidity was removed, in the terms of the quote token converted to US dollar

start_block: tradingstrategy.types.BlockNumber

Blockchain tracking information

end_block: tradingstrategy.types.BlockNumber

Blockchain tracking information

classmethod to_pyarrow_schema(small_candles=False) pyarrow.lib.Schema

Construct schema for writing Parquet filess for these candles.

Parameters

small_candles – Use even smaller word sizes for frequent (1m) candles.

__init__(pair_id: tradingstrategy.types.PrimaryKey, timestamp: tradingstrategy.types.UNIXTimestamp, exchange_rate: float, open: tradingstrategy.types.USDollarAmount, close: tradingstrategy.types.USDollarAmount, high: tradingstrategy.types.USDollarAmount, low: tradingstrategy.types.USDollarAmount, adds: int, removes: int, syncs: int, add_volume: tradingstrategy.types.USDollarAmount, start_block: tradingstrategy.types.BlockNumber, end_block: tradingstrategy.types.BlockNumber) None
class tradingstrategy.liquidity.LiquidityResult

Server-reply for live queried liquidity data.

liquidity_events: List[tradingstrategy.liquidity.XYLiquidity]

A bunch of candles. Candles are unordered and subject to client side sorting. Multiple pairs and chains may be present in candles.

sort_by_timestamp()

In-place sorting of candles by their timestamp.

__init__(liquidity_events: List[tradingstrategy.liquidity.XYLiquidity]) None
class tradingstrategy.liquidity.GroupedLiquidityUniverse

A universe where each trading pair has its own liquidity data feed.

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`GroupedLiquidityUniverse` creates trading pair id -> liquidity sample data grouping out from raw liquidity sample.

get_liquidity_samples_by_pair(pair_id: tradingstrategy.types.PrimaryKey) Optional[pandas.core.frame.DataFrame]

Get samples for a single pair.