Topics

#api #blockchain #cex #coingecko #crypto #cryptocurrency #dapp #defi #dex #exchanges #farming #impermanent-loss #pancakeswap #stake

Language breakdown

Python 100.0%

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README

DeFi open source tools

Get Started

Instalation

General Tools

Impermanent Loss, simple calculation
Compare Buy & Hold with Staking and Farming
Complete list for DeFi protocols TVL, volume and more
Example listing top20 DeFi dapps by TVL
Example show historical TVL for one or more protocols

PancakeSwap API

All token prices real time
All pairs liquidity, volume and more
Get data in real time for one token
Get data in real time for one pair
Simulate invest in LP considering impermanent loss, 3D graph

Get started

Instalation

pip install defi

Impermanent Loss

import defi.defi_tools as dft
Impermanent loss for stableCoin & -20% return token 
dft.iloss(0.8)

-0.62%

import defi.defi_tools as dft
Impermanent loss for stableCoin & +60% return token 
dft.iloss(1.6, numerical=True)

0.027 # Same as 2.7%

Buy&Hold vs Stake & Farming strategy

import defi.defi_tools as dft
Exercise: Get returns after 20 days, assuming token A is a stablecoin, token B perform + 150%
individual staking pools for both = 0.01% & 0.05% daily
liquidity-pool farming rewards =0.2% daily & Earn by fees/day = 0.01%
dft.compare(days=20, varA=0, varB=150, rwpoolA=0.01, rwpoolB=0.05, rwpoolAB=0.2, fees_AB=0.01)

{
 "buy_hold": "75.00%",
 "stake": "75.60%",
 "farm": "71.96%",
 "Best": "Stake"
}

DeFi protocols

import defi.defi_tools as dft
metadata, df = dft.getProtocol('Uniswap') metadata

{
 "id": "1",
 "name": "Uniswap",
 "address": "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984",
 "symbol": "UNI",
 "url": "https://info.uniswap.org/",
 "description": "A fully decentralized protocol for automated liquidity provision on Ethereum.\r\n",
 "chain": "Ethereum",
 "logo": "None",
 "audits": "2",
 "audit_note": "None",
 "gecko_id": "uniswap",
 "cmcId": "7083",
 "category": "Dexes",
 "chains": ["Ethereum"],
 "module": "uniswap.js"
}

Top 20 dapps TVL by chain

import defi.defi_tools as dft
import matplotlib.pyplot as plt
df = dft.getProtocols() fig, ax = plt.subplots(figsize=(12,6))
n = 50 # quantity to show top = df.sort_values('tvl', ascending=False).head(n)
chains = top.groupby('chain').size().index.values.tolist() for chain in chains:     filtro = top.loc[top.chain==chain]     ax.bar(filtro.index, filtro.tvl, label=chain)
ax.set_title(f'Top {n} dApp TVL, groupBy dApp main Chain', fontsize=14) ax.grid(alpha=0.5) plt.legend() plt.xticks(rotation=90) plt.show()

Historical TVL

import defi.defi_tools as dft
import pandas as pd
exchanges = ['pancakeswap', 'curve', 'makerdao', 'uniswap','Compound', 'AAVE','sushiswap','anchor']
hist = [dft.getProtocol(exchange)[1] for exchange in exchanges] df = pd.concat(hist, axis=1) df.columns = exchanges
df.plot(figsize=(12,6))

CoinGecko API

Endpoints available, some examples:

* dft.getGeckoIDs() # coinGecko first 5000 ids

* dft.geckoPrice("bitcoin,ethereum", "usd,eur,brl") # coinGecko quotes

* dft.geckoList(page=1, per_page=250) # full coinGecko cyptocurrency list

* dft.geckoMarkets("ethereum") # top 100 liquidity markets, prices, and more, for eth or other coin

* dft.geckoHistorical('cardano') # full history containing price, market cap and volume

* dft.farmSimulate(['huobi-token','tether'], apr=45) # Simulate farming strategy with apr=45%

CoinGecko - ids list

import defi.defi_tools as dft
ids = dft.getGeckoIDs() ids[:10]

 ['bitcoin',  'ethereum',  'binancecoin',  'tether',  'solana',  'cardano',  'ripple',  'polkadot',  'shiba-inu',  'dogecoin']

CoinGecko - Get price for coins at diferent currencies

import defi.defi_tools as dft
dft.geckoPrice("bitcoin,ethereum", "usd,eur,brl")

{"ethereum": {"usd": 2149.85, "eur": 1807.58, "brl": 12208.77},
 "bitcoin": {"usd": 60188, "eur": 50606, "brl": 341802}}

CoinGecko - Get main exchanges for a coin or token

import defi.defi_tools as dft
df = dft.geckoMarkets("ethereum") print(df.info()) returns top 100 ethereum quotes by volume

Index: 100 entries, IDCM to FTX.US Data columns (total 9 columns):  #   Column       Non-Null Count  Dtype               ---  ------       --------------  -----                0   base         100 non-null    object               1   target       100 non-null    object               2   last         100 non-null    float64              3   volume       100 non-null    float64              4   spread       100 non-null    float64              5   timestamp    100 non-null    datetime64[ns, UTC]  6   volume_usd   100 non-null    float64              7   price_usd    100 non-null    float64              8   trust_score  100 non-null    object              dtypes: datetime64ns, UTC, float64(5), object(3) memory usage: 7.8+ KB

CoinGecko - historical prices for a coin

import defi.defi_tools as dft
df = dft.geckoHistorical('cardano') print(df)

price marketcaps totalvolumes date 2017-10-18 00:00:00 0.026845 6.960214e+08 2.351678e+06 2017-10-19 00:00:00 0.026830 6.956220e+08 2.815156e+06 2017-10-20 00:00:00 0.030300 7.855800e+08 8.883473e+06 2017-10-21 00:00:00 0.028588 7.412021e+08 5.308857e+06 2017-10-22 00:00:00 0.027796 7.206698e+08 2.901876e+06 ... ... ... ... 2021-04-13 00:00:00 1.319790 4.223483e+10 5.005258e+09 2021-04-14 00:00:00 1.422447 4.565529e+10 5.693373e+09 2021-04-15 00:00:00 1.456105 4.676570e+10 8.920293e+09 2021-04-16 00:00:00 1.478071 4.730118e+10 5.151595e+09 2021-04-17 03:47:55 1.433489 4.595961e+10 5.152747e+09

[1278 rows x 3 columns]

CoinGecko - Farming Simulate

import defi.defi_tools as dft
pair = ['huobi-token','tether'] apr = 45
dft.farmSimulate(pair, apr, start='2021-01-01')

Downloading huobi-token Downloading tether {'Token 1': 'huobi-token', 'Token 2': 'tether', 'start': '2021-01-01', 'fixed APR': '45%', 'Buy & Hold': '68.90%', 'Impermanent Loss': '-8.66%', 'Farming Rewards': '75.45%', 'Farming + Rewards - IL': '153.02%'}

PancakeSwap - Get tokens prices in real time

import defi.defi_tools as dft
df = dft.pcsTokens() print(df)

name symbol price price_BNB updated 0x0E09FaBB73Bd3Ade0a17ECC321fD13a19e81cE82 PancakeSwap Token Cake 24.0636 0.0450 2021-04-17 04:29:08.332 0xbb4CdB9CBd36B01bD1cBaEBF2De08d9173bc095c Wrapped BNB WBNB 534.2575 1.0000 2021-04-17 04:29:08.332 0x0F9E4D49f25de22c2202aF916B681FBB3790497B Perlin PRL 0.2091 0.0004 2021-04-17 04:29:08.332 0xe9e7CEA3DedcA5984780Bafc599bD69ADd087D56 BUSD Token BUSD 1.0000 0.0019 2021-04-17 04:29:08.332 0x7130d2A12B9BCbFAe4f2634d864A1Ee1Ce3Ead9c BTCB Token BTCB 62166.5517 116.3604 2021-04-17 04:29:08.332 ... ... ... ... ... ... 0xB6802C06A441BA63624751C53C7c0708b75F06EC FinalMoon FINALMOON 0.0651 0.0001 2021-04-17 04:29:08.332 0x2cF0DA1EB4165d73156CE1E32450e4A0E1c1791b FairUnicorn FUni 0.0000 0.0000 2021-04-17 04:29:08.332 0x5CeD26185f82B07E1516d0B013c54CcBD252A4Ad Peaches PEACH 0.1130 0.0002 2021-04-17 04:29:08.332 0x2bA64EFB7A4Ec8983E22A49c81fa216AC33f383A Wrapped BGL WBGL 0.1000 0.0002 2021-04-17 04:29:08.332 0x019bE1796178516e060072004F267B59a49A0801 Pepper Finance PEPR 0.1819 0.0003 2021-04-17 04:29:08.332

[854 rows x 5 columns]

PancakeSwap - Get pairs, liquidity, and more

import defi.defi_tools as dft
pairs = dft.pcsPairs(as_df=False) print(pairs)

{"updated_at": 1618645355351,
 "data": {"0x0E09FaBB73Bd3Ade0a17ECC321fD13a19e81cE82_0xbb4CdB9CBd36B01bD1cBaEBF2De08d9173bc095c": 
 	{"pair_address": "0xA527a61703D82139F8a06Bc30097cC9CAA2df5A6",
	   "base_name": "PancakeSwap Token",
	   "base_symbol": "Cake",
	   "base_address": "0x0E09FaBB73Bd3Ade0a17ECC321fD13a19e81cE82",
	   "quote_name": "Wrapped BNB",
	   "quote_symbol": "WBNB",
	   "quote_address": "0xbb4CdB9CBd36B01bD1cBaEBF2De08d9173bc095c",
	   "price": "0.04503969270521829587",
	   "base_volume": "5473068.824002232134035221",
	   "quote_volume": "239997.1228321299572591638",
	   "liquidity": "1076144814.0632013827775993748053",
	   "liquidity_BNB": "2007551.221740467021401314"
	},
}

PancakeSwap - Get token info

import defi.defi_tools as dft
dft.pcsTokenInfo('cake')

{"name": "PancakeSwap Token",
 "symbol": "Cake",
 "price": "24.03353223898417117634582253598019",
 "price_BNB": "0.04503467915973850237292527741402623"
}

PancakeSwap - Get pair info

import defi.defi_tools as dft
dft.pcsPairInfo('cake','bnb')

{"pair_address": "0xA527a61703D82139F8a06Bc30097cC9CAA2df5A6",
 "base_name": "PancakeSwap Token",
 "base_symbol": "Cake",
 "base_address": "0x0E09FaBB73Bd3Ade0a17ECC321fD13a19e81cE82",
 "quote_name": "Wrapped BNB",
 "quote_symbol": "WBNB",
 "quote_address": "0xbb4CdB9CBd36B01bD1cBaEBF2De08d9173bc095c",
 "price": "0.04503969270521829587",
 "base_volume": "5473068.824002232134035221",
 "quote_volume": "239997.1228321299572591638",
 "liquidity": "1076144814.0632013827775993748053",
 "liquidity_BNB": "2007551.221740467021401314"
}

PancakeSwap - Simulate LP invest

import defi.defi_tools as dft
dft.valuef, iloss = dft.ilosssimulate('cake','bnb', value=1000, basepctchg=50, quotepctchg=-25)

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