%matplotlib inline import matplotlib.pylab as plt; import matplotlib.ticker as mtick from datetime import datetime, timedelta, datetime, date import pandas as pd; import numpy as np import datetime as dt from ftx import FTX, apy, settings; ftx = FTX(**settings[account]);

filled = ftx.paginate(f'/fills', {"limit": 5000}, "time") lending = ftx.paginate(f'/spot_margin/lending_history', {"limit": 5000}, "time") borrow = ftx.paginate(f'/spot_margin/borrow_history', {"limit": 200}, "time") funding = ftx.paginate(f'/funding_payments', {"limit": 5000}, "time") airdrops = ftx.paginate(f'/wallet/airdrops', {"limit": 200}, "time") deposits = ftx.paginate(f'/wallet/deposits', {"limit": 200}, "time") withdrawals = ftx.paginate(f'/wallet/withdrawals', {"limit": 200}, "time") positions = pd.DataFrame(ftx.request(f'/positions')) wallet = pd.DataFrame(ftx.request(f'/wallet/all_balances')['main'])

def save(*args, ): df, name = args # filename = f'./export/ftx_{name}_{datetime.now().strftime("%Y-%m-%d")}.csv' filename = f'./export/{name}.csv' df.to_csv(filename, index=False) data = [ (filled, 'FTX_h21_O'), [lending, 'FTX_h21_L'], [borrow, 'FTX_h21_B'], [funding, 'FTX_h21_F'], [airdrops, 'FTX_h21_A'], [deposits.drop('address',1), "FTX_h21_D"], [withdrawals, "FTX_h21_W"], [positions, "FTX_h21_positions"], [wallet, "FTX_h21_wallet"] ] for (df, name) in data: save(df, name); if ("id" in df.columns): duplicates = df.pipe(lambda x: x.loc[x.duplicated("id")]) if len(duplicates): print(duplicates)

def group(df, freq, by=['market', 'time']): if freq and not 'time' in by: by += 'time'; grouped = df.set_index(by)\ .groupby([col if col != 'time' else pd.Grouper(freq=freq, level=by.index('time')) for col in by])\ .agg({'rate': np.sum, 'payment': np.sum})\ .unstack(0) #print(grouped) return grouped df = funding ax=df.pipe(group, freq='1H', by=['time'])['rate']\ .rolling(7, min_periods=1).mean()\ .plot(figsize=(16, 6), title='Average hourly funding rate') [ax.set_yticklabels([f"{1e0*x:.4f}" for x in ax.get_yticks()])];

df.payment.sum()