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Feat: Add a helper function that adds price to a swap events dataframe #9

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f629ef4
Add friendly typecasts when reading swap events and mint/burn events
mud2monarch May 1, 2024
139ecb8
change float casts to int casts when possible
mud2monarch May 2, 2024
27e5597
Add a README with data schemas post-cast.
mud2monarch May 2, 2024
360c2bd
OCD change
mud2monarch May 2, 2024
4c0591a
add helper function to convert sqrtPriceX96 to price
mud2monarch May 13, 2024
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46 changes: 46 additions & 0 deletions v3/README.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1,46 @@
The function `readFromMemoryOrDisk` intakes a parquet (as components of a filepath) and outputs a Polars dataframe. After running the function, here is the schema for `pool_mint_burn_events` and `pool_swap_events` dataframes.

**Swaps**
| Column | Polars datatype |
|-------------------|-----------------|
| chain_name | str |
| address | str |
| block_timestamp | datetime[MiS] |
| block_number | i64 |
| transaction_hash | str |
| sender | str |
| recipient | str |
| amount0 | f64 |
| amount1 | f64 |
| sqrtPriceX96 | f64 |
| liquidity | f64 |
| tick | i32 |
| from_address | str |
| to_address | str |
| transaction_index | i64 |
| gas_price | i64 |
| gas_used | i64 |
| l1_fee | i64 |

**Mint & Burn**
| Column | Polars datatype |
|-------------------|-----------------|
| chain_name | str |
| address | str |
| block_timestamp | datetime[us] |
| block_number | i64 |
| transaction_hash | str |
| log_index | i64 |
| amount | f64 |
| amount0 | f64 |
| amount1 | f64 |
| owner | str |
| tick_lower | i64 |
| tick_upper | i64 |
| type_of_event | f64 |
| to_address | str |
| from_address | str |
| transaction_index | i64 |
| gas_price | i64 |
| gas_used | i64 |
| l1_fee | i64 |
13 changes: 13 additions & 0 deletions v3/helpers/swap_math.py
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Expand Up @@ -151,3 +151,16 @@ def get_next_sqrtPrice(ratioA, liq, amount, zeroForOne):
sqrtPrice_next = get_next_price_amount1(ratioA, liq, amount, zeroForOne)

return sqrtPrice_next

def addPrice(
data: pl.DataFrame
) -> pl.DataFrame:
"""
Helper function to add a new column with sqrtPriceX96 converted to price
"""
sqrtPrice_list = data.select(pl.col("sqrtPriceX96")).to_series().to_list()
price_list = [(int(i)/(2 ** 96))**2 for i in sqrtPrice_list]
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i think i would prefer floats here. sometimes prices are very small because they are inverted to your expectation/high supply.

i think you should also be able to apply map_batches


df = data.with_columns(price = pl.Series(values=price_list, dtype=pl.UInt64))
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also float here


return df
19 changes: 19 additions & 0 deletions v3/state.py
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Expand Up @@ -131,6 +131,19 @@ def readFromMemoryOrDisk(self, data, data_path, save=False):
.with_columns(
as_of=pl.col("block_number") + pl.col("transaction_index") / 1e4
)
.cast(
{
"block_timestamp":pl.Datetime,
"amount0":pl.Float64,
"amount1":pl.Float64,
"sqrtPriceX96":pl.Float64,
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heres as well since X96 is also a uint160

"liquidity":pl.Float64,
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might be nice to see if this is convertible to a no precision loss value since liquidity is must be less than an int128, but in practice is quite a bit smaller than that (due to the max liquidity in tick math)

"tick":pl.Int32, # tick in v3 is int24
"gas_price":pl.Int64,
"gas_used":pl.Int64,
"l1_fee":pl.Int64
}
)
.collect()
.sort("as_of")
)
Expand All @@ -156,6 +169,12 @@ def readFromMemoryOrDisk(self, data, data_path, save=False):
"tick_lower": pl.Int64,
"tick_upper": pl.Int64,
"type_of_event": pl.Float64,
"block_timestamp":pl.Datetime,
"amount0":pl.Float64,
"amount1":pl.Float64,
"gas_price":pl.Int64,
"gas_used":pl.Int64,
"l1_fee":pl.Int64
}
)
.with_columns(
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