R Package to Interact and Trade with Uniswap Platform and Exploration of Uniswap data from GraphQL.
Install Using
devtools::install_github("OmniacsDAO/uniswappeR")
The package has 3 Functionalities
Our uniswappeR
R package includes the functionality to trade and query prices from the Uniswap Platform. To interact with the Uniswap Platform
we need to configure the environment and then we can use the functions to make trades on the uniswap platform and query prices.
View the video walkthrough on how to conduct trades through UniswappeR here.
library(reticulate)
py_discover_config()
to figure the path for python3use_python("/usr/local/bin/python3")
virtualenv_create("uniswappeR-env")
virtualenv_install(envname="uniswappeR-env",packages=c("uniswap-python"))
use_virtualenv("uniswappeR-env",required=TRUE)
Web3_checksum <- import("web3",convert=FALSE)$Web3$to_checksum_address
library(reticulate)
library(uniswappeR)
use_virtualenv("uniswappeR-env",required=TRUE)
Web3_checksum <- import("web3",convert=FALSE)$Web3$to_checksum_address
set_infura_node("https://mainnet.infura.io/v3/XXXXXXXXXXXXXXXXXXX")
u_w <- uniswap_session(user_add = "**", pvt_key = "***", version = 3)
We would use Uniswap(UNI)/ETH pair to query on
t_a <- Web3_checksum("0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
t_d <- 18
We would use Uniswap(UNI)/ DAI Stablecoin pair to query for Token to Token swap
t1_a <- Web3_checksum("0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
t1_d <- 18
t2_a <- Web3_checksum("0x6b175474e89094c44da98b954eedeac495271d0f")
t2_d <- 18
Check Your ETH Balance
check_eth_balance(u_w)
Check Your Uniswap (UNI) Token Balance
check_tok_balance(t_a,t_d,u_w)
How much UNI Token you would get for .5 ETH, When you Swap ETH for UNI
e_q <- .5
check_eth.to.tok_eth.fix(t_a,t_d,e_q,u_w)
How much ETH you need to get 2 UNI Tokens, When you Swap ETH for UNI
t_q <- 2
check_eth.to.tok_tok.fix(t_a,t_d,t_q,u_w)
How much ETH you would get for 2 UNI Tokens, When you Swap UNI for ETH
t_q <- 2
check_tok.to.eth_tok.fix(t_a,t_d,t_q,u_w)
How much UNI Token you need to get .5 ETH Tokens, When you Swap UNI for ETH
e_q <- .5
check_tok.to.eth_eth.fix(t_a,t_d,e_q,u_w)
How much DAI Token you would get for 2 UNI, When you Swap UNI for DAI (Uses UNI->ETH->DAI Route)
t1_q <- 2
check_tok1.to.tok2_tok1.fix(t1_a,t1_d,t2_a,t2_d,t1_q,u_w)
How much UNI Token you would need to get 50 DAI, When you Swap UNI for DAI (Uses UNI->ETH->DAI Route)
t2_q <- 50
check_tok1.to.tok2_tok2.fix(t1_a,t1_d,t2_a,t2_d,t2_q,u_w)
We would use Uniswap(UNI)/ETH pair to make swaps
t_a <- Web3_checksum("0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
t_d <- 18
We would use Uniswap(UNI)/ DAI Stablecoin pair for Token to Token swap
t1_a <- Web3_checksum("0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
t1_d <- 18
t2_a <- Web3_checksum("0x6b175474e89094c44da98b954eedeac495271d0f")
t2_d <- 18
Swap .5 ETH for UNI Tokens
e_q <- .5
trade_eth.to.tok_eth.fix(t_a,t_d,e_q,u_w)
Swap as much ETH required to get 2 UNI Tokens
t_q <- .5
trade_eth.to.tok_tok.fix(t_a,t_d,t_q,u_w)
Swap 2 UNI Tokens for ETH
t_q <- 2
trade_tok.to.eth_tok.fix(t_a,t_d,t_q,u_w)
Swap as much UNI Tokens required to get .5 ETH
e_q <- .5
trade_tok.to.eth_eth.fix(t_a,t_d,e_q,u_w)
Swap 2 UNI Tokens for DAI Tokens
t1_q <- 2
trade_tok1.to.tok2_tok1.fix(t1_a,t1_d,t2_a,t2_d,t1_q,u_w)
Swap as much UNI Tokens required to get 20 DAI Tokens
t2_q <- 20
trade_tok1.to.tok2_tok2.fix(t1_a,t1_d,t2_a,t2_d,t2_q,u_w)
Our uniswappeR
R package contains the codebase that abstracts away the GraphQL layer of querying for uniswap data into a user-friendly R package.
View the video walkthrough on how to pull data into R using UniswappeR.
library(uniswappeR)
factory_stats_v2()
factory_stats_v3()
uniswap_stats_hist_v2()
uniswap_stats_hist_v3()
token_stats_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
token_stats_v3(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
token_stats_hist_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
token_stats_hist_v3(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
token_pair_map_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
token_pair_map_v3(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
pairs_all_v2()
pairs_all_v3()
pair_stats_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_stats_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_stats_hist_hourly_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_stats_hist_hourly_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_stats_hist_daily_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_stats_hist_daily_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_liq_positions_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_liq_positions_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_liq_positions_hist_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_liq_positions_hist_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_mint_txs_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_mint_txs_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_burn_txs_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_burn_txs_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
pair_swap_txs_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
pair_swap_txs_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
user_lps_v2(user_address = "0x2502f65d77ca13f183850b5f9272270454094a08")
user_lps_v3(user_address = "0xF1c206dd83ee2b8E6Ea675Cf827C93c58486B972")
user_hist_lps_v2(user_address = "0x2502f65d77ca13f183850b5f9272270454094a08")
user_hist_lps_v3(user_address = "0xF1c206dd83ee2b8E6Ea675Cf827C93c58486B972")
user_swaps_v2(user_address = "0xcd8aa390e6eabbd2169b3580c1f7ce854675fd03")
user_swaps_v3(user_address = "0x431B5A84aCC1297Eda88259f300262F1bc3A74f3")
user_mints_v2(user_address = "0xcd8aa390e6eabbd2169b3580c1f7ce854675fd03")
user_mints_v3(user_address = "0x431B5A84aCC1297Eda88259f300262F1bc3A74f3")
user_burns_v2(user_address = "0xcd8aa390e6eabbd2169b3580c1f7ce854675fd03")
user_burns_v3(user_address = "0x431B5A84aCC1297Eda88259f300262F1bc3A74f3")
data_to_export <- token_stats_hist_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
path_to_export <- "~/Desktop/uniswappeR_export.csv"
export_data(data_to_export,path_to_export)
Our uniswappeR
R package includes a number of high level functions for interacting and visualising the data from uniswap:
The below code snippet provides a forecast using geometric Brownian motion its tunable parameters are the Pair's Address, forecast window, max % increase that can occur in a single day, and number of simulations, respectively. The resulting output is a data frame with the liquidity range forecast.
liquidity_range_all_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801",days=30,cap=10,sims=1000)
This provides the suggested range for the liquidity.
liquidity_range_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
Allows for the visualisation of the GBM process for a Pair's address. Additionally ggplot
pipelines can be added to modify the visualisation.
liquidity_range_visualization("0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
This section introduces various visualisations within uniswappeR
.
This allows the user to visualise various growth metrics of the UniswapV2 Platform. From this, insights about daily volume, liquidity, and transaction counts can be analyzed. For example, we can infer that the amount of transactions overtime is steadily increasing, and that there is not a clear, discernible pattern for the daily volume nor liquidity.
vis_uniswap_stats_hist_v2()
This allows the user to visualise various growth metrics of the UniswapV3 Platform. From this, insights about daily volume, liquidity, and transaction counts can be analyzed. This visual quickly allows us to see that sometime around March 2022, a large spike occured for the daily volume. This this enables the curious user to explore in further detail the events that occurred that day.
vis_uniswap_stats_hist_v3()
This allows the user to visualise various growth metrics of a given token in UniswapV2. Similar to the other historical stats functions, this allows the user to see past trends for a specific token on UniswapV2. From this we can quickly notice a downward trend in token liquidity signaling a rush on that token.
vis_token_stats_hist_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
This allows the user to visualise various growth metrics of a given token in UniswapV3. Similar to the other historical stats functions, this allows the user to see past trends for a specific token on UniswapV3. One quick insight we can gather is that there appears to be a progressively less popularity in this token's trade volume which is allow, perhaps, correlating with the token's price.
vis_token_stats_hist_v3(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
The below visual provides an indication of how often a given token is in a pair where token0 and token1 are given by the sorting order.
vis_token_pair_map_v2(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
The below visual provides an indication of how often a given token is in a pair where token0 and token1 are given by the sorting order.
vis_token_pair_map_v3(token_address = "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984")
The following provides a vasual of various growth metrics of a given pair in UniswapV2. In this example the pair_address is UNI to DAI.
vis_pair_stats_hist_daily_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
The following provides a vasual of various growth metrics of a given pair in UniswapV3.
vis_pair_stats_hist_daily_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
Allows for a user to visualise liquidity positions spread in a given pair via a histogram. One insight we can gather from this is that one address is providing about 1450 tokens to the LP whereas most others are contributing substaintially less.
vis_pair_liq_positions_v2(pair_address = "0xf00e80f0de9aea0b33aa229a4014572777e422ee")
vis_pair_liq_positions_v3(pair_address = "0x1d42064fc4beb5f8aaf85f4617ae8b3b5b8bd801")
Allows a user to retrieve all the swaps data for a given address or addresses. This allows a user to readily recognize their level of activity.
addresses <- c("0x2e3381202988d535e8185e7089f633f7c9998e83", "0x4d9c274ADF71e4201B4aB1f28BF05D44eE4bA261")
swap_data <- swaps(addresses)
swap_visualizations(swap_data)
plot_to_export <- vis_uniswap_stats_hist_v2()
path_to_export <- "~/Desktop/uniswappeR_plot_export.png"
export_plot(plot_to_export,path_to_export)
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