defi
Advanced tools
pip install defi
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%
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, var_A=0, var_B=150, rw_pool_A=0.01, rw_pool_B=0.05, rw_pool_AB=0.2, fees_AB=0.01)
{
"buy_hold": "75.00%",
"stake": "75.60%",
"farm": "71.96%",
"Best": "Stake"
}
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"
}
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()
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))
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%
import defi.defi_tools as dft
ids = dft.getGeckoIDs()
ids[:10]
['bitcoin', 'ethereum', 'binancecoin', 'tether', 'solana', 'cardano', 'ripple', 'polkadot', 'shiba-inu', 'dogecoin']
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}}
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: datetime64[ns, UTC](1), float64(5), object(3)
memory usage: 7.8+ KB
import defi.defi_tools as dft
df = dft.geckoHistorical('cardano')
print(df)
price market_caps total_volumes 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]
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%'}
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]
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"
},
}
import defi.defi_tools as dft
dft.pcsTokenInfo('cake')
{"name": "PancakeSwap Token",
"symbol": "Cake",
"price": "24.03353223898417117634582253598019",
"price_BNB": "0.04503467915973850237292527741402623"
}
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"
}
import defi.defi_tools as dft
dft.value_f, iloss = dft.iloss_simulate('cake','bnb', value=1000, base_pct_chg=50, quote_pct_chg=-25)
FAQs
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