Loading exchange, pair, and candlestick data for Uniswap V3#
In this notebook, we create a basic example of how to load:
Exchanges
Pairs
Candle data
We use the TradingStrategy API to get data for 3 pairs on Uniswap V3
This is done for 3 pairs: eth_usdc, eth_usdt, and usdt_usdc
The following code block shows an example of some the data from the api
Load exchanges and pairs#
First, let’s create a TradingStrategy dataset client#
[1]:
from tradingstrategy.client import Client
client = Client.create_jupyter_client()
Started Trading Strategy in Jupyter notebook environment, configuration is stored in /home/alex/.tradingstrategy
Download exchange and pair data#
[2]:
from tradingstrategy.exchange import ExchangeUniverse
from pyarrow import Table
# Exchange map data is so small it does not need any decompression
exchange_universe: ExchangeUniverse = client.fetch_exchange_universe()
# Decompress the pair dataset to Python map
columnar_pair_table: Table = client.fetch_pair_universe()
print(f"Total pairs {len(columnar_pair_table)}, total exchanges {len(exchange_universe.exchanges)}")
Total pairs 172857, total exchanges 4431
[3]:
pairs_df = columnar_pair_table.to_pandas()
print(pairs_df.head())
pair_id chain_id exchange_id address \
0 1 1 1 0xb4e16d0168e52d35cacd2c6185b44281ec28c9dc
1 3 1 1 0x12ede161c702d1494612d19f05992f43aa6a26fb
2 4 1 1 0xa478c2975ab1ea89e8196811f51a7b7ade33eb11
3 5 1 1 0x07f068ca326a469fc1d87d85d448990c8cba7df9
4 6 1 1 0xae461ca67b15dc8dc81ce7615e0320da1a9ab8d5
token0_address \
0 0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48
1 0x06af07097c9eeb7fd685c692751d5c66db49c215
2 0x6b175474e89094c44da98b954eedeac495271d0f
3 0x408e41876cccdc0f92210600ef50372656052a38
4 0x6b175474e89094c44da98b954eedeac495271d0f
token1_address token0_symbol token1_symbol \
0 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 USDC WETH
1 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 CHAI WETH
2 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 DAI WETH
3 0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48 REN USDC
4 0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48 DAI USDC
dex_type base_token_symbol ... sell_count_all_time buy_volume_all_time \
0 uniswap_v2 WETH ... 2327412.0 2.162229e+10
1 uniswap_v2 CHAI ... 349.0 6.703155e+04
2 uniswap_v2 WETH ... 748406.0 7.822935e+09
3 uniswap_v2 REN ... 564.0 4.783008e+04
4 uniswap_v2 DAI ... 86257.0 2.858813e+08
sell_volume_all_time buy_count_30d sell_count_30d buy_volume_30d \
0 2.144116e+10 24031.0 18386.0 4.362241e+07
1 6.941784e+04 NaN NaN NaN
2 7.720916e+09 3185.0 2928.0 5.676228e+06
3 4.963640e+04 NaN NaN NaN
4 2.849519e+08 348.0 354.0 4.752193e+05
sell_volume_30d buy_tax transfer_tax sell_tax
0 4.428402e+07 0.0 0.0 0.0
1 NaN 999.0 999.0 999.0
2 5.855924e+06 0.0 0.0 0.0
3 NaN 999.0 999.0 999.0
4 4.767471e+05 0.0 0.0 0.0
[5 rows x 36 columns]
Filtering pairs to have 5 BPS fee on Uniswap V3#
[4]:
from tradingstrategy.stablecoin import ALL_STABLECOIN_LIKE
import pandas as pd
from tradingstrategy.pair import filter_for_stablecoins
from tradingstrategy.pair import StablecoinFilteringMode
fee_tier = 5 # BPS
low_fee_pairs: pd.DataFrame = pairs_df.loc[
(pairs_df["exchange_slug"] == "uniswap-v3") &
(pairs_df["fee"] == fee_tier) # BPS
]
print(f"Found {len(low_fee_pairs)} total pairs at {fee_tier} BPS fee tier")
# Filter out stablecoin pairs,
# because trading dollars to dollars does not make trading sense (unless in arbitrage, which is not covered here)
low_fee_pairs = filter_for_stablecoins(low_fee_pairs, StablecoinFilteringMode.only_volatile_pairs)
print(f"Found {len(low_fee_pairs)} volatile pairs quoted in any token")
stablecoin_quoted_pairs = low_fee_pairs.loc[low_fee_pairs["quote_token_symbol"].isin(ALL_STABLECOIN_LIKE)]
print(f"Found {len(stablecoin_quoted_pairs)} volatile pairs quoted in a stablecoin")
# Assume no volume data is zero volume
stablecoin_quoted_pairs = stablecoin_quoted_pairs.fillna(0)
volume_threshold_30d = 1_000_000
volume_pairs = stablecoin_quoted_pairs.loc[stablecoin_quoted_pairs["buy_volume_30d"] >= volume_threshold_30d]
print(f"Found {len(volume_pairs)} pairs with enough volume")
Found 382 total pairs at 5 BPS fee tier
Found 317 volatile pairs quoted in any token
Found 161 volatile pairs quoted in a stablecoin
Found 15 pairs with enough volume
Get candle data using API#
This is an example of gettting data directly from the TradingStrategy API. Visit the API explorer for the full API documentation.
Note: This is a slow method and shouldn’t be used for actual backtesting purposes. For backtesting, candle data comes from the TradingStrategyUniverse (created by the create_trading_universe function). Visit the strategy examples section for backtesting examples.
[5]:
import requests
import pandas as pd
pairs = {
'eth_usdc': 'https://tradingstrategy.ai/api/candles?pair_id=2697765&exchange_type=uniswap_v3&time_bucket=4h',
'usdt_usdc': 'https://tradingstrategy.ai/api/candles?pair_id=2711887&exchange_type=uniswap_v3&time_bucket=4h',
'eth_usdt': 'https://tradingstrategy.ai/api/candles?pair_id=2697770&exchange_type=uniswap_v3&time_bucket=4h'
}
def get_candles(url: str) -> pd.DataFrame:
x = requests.get(url)
json = x.json()
candles = pd.DataFrame.from_dict(json)
candles.rename(columns = {'ts':'date','o':'open', 'h':'high','l':'low','c':'close','v':'volume'}, inplace = True)
candles['timestamp'] = pd.to_datetime(candles['date'])
candles = candles.set_index('date')
return candles
pair_data = dict([ (k,get_candles(r)) for k,r in pairs.items()])
pair_data['usdt_usdc'].head()
[5]:
| open | high | low | close | volume | xr | b | s | tc | bv | sv | timestamp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | ||||||||||||
| 2023-05-22T20:00:00 | 1.0003 | 1.0003 | 1.0003 | 1.0003 | 4.419615e+06 | 1.0 | None | None | None | None | None | 2023-05-22 20:00:00 |
| 2023-05-23T00:00:00 | 1.0003 | 1.0003 | 1.0003 | 1.0003 | 5.208769e+06 | 1.0 | None | None | None | None | None | 2023-05-23 00:00:00 |
| 2023-05-23T04:00:00 | 1.0003 | 1.0003 | 1.0003 | 1.0003 | 2.442417e+07 | 1.0 | None | None | None | None | None | 2023-05-23 04:00:00 |
| 2023-05-23T08:00:00 | 1.0003 | 1.0003 | 1.0003 | 1.0003 | 2.184966e+07 | 1.0 | None | None | None | None | None | 2023-05-23 08:00:00 |
| 2023-05-23T12:00:00 | 1.0003 | 1.0003 | 1.0003 | 1.0003 | 2.106995e+07 | 1.0 | None | None | None | None | None | 2023-05-23 12:00:00 |
Visualize candlestick data#
Here we visualize the prices and volumes of each of the 3 pairs.
[6]:
from tradingstrategy.charting.candle_chart import visualise_ohlcv
def get_figure(candles: pd.DataFrame, chart_name: str):
return visualise_ohlcv(
candles,
height=600,
theme="plotly_white",
chart_name=chart_name,
y_axis_name="Price",
volume_axis_name="volume",
)
figures = dict([ (k,get_figure(r, k)) for k,r in pair_data.items()])
figures['eth_usdc'].show()
figures['eth_usdt'].show()
figures['usdt_usdc'].show()