stevekeyharvey/Funding_Rate
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refactor valkey into objects with health check

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stevekeyharvey
2026-05-15 11:34:25 +09:00
parent f5f43be1a1
commit 1fd922d98f
25 changed files with 6461 additions and 9613 deletions
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47
engine_best_funding_rate.py
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@@ -93,7 +93,10 @@ async def get_aster_exch_info() -> pd.DataFrame:
return df
def load_aster_current_fr(df_aster_exch_info: pd.DataFrame) -> pd.DataFrame:
df = pd.DataFrame(data=json.loads(s=VAL_KEY.get(name='fund_rate_aster_all'))) # ty:ignore[invalid-argument-type]
vk_get: str = VAL_KEY.get(name='fund_rate_aster_all') # ty:ignore[invalid-assignment]
if not vk_get:
raise ValueError(f'fund_rate_aster_all is empty: {vk_get}')
df = pd.DataFrame(data=json.loads(vk_get))
df: pd.DataFrame = df[['s','E','r','T']].rename({'s':'symbol','E':'funding_rate_updated_ts_ms','r':'funding_rate','T':'next_funding_ts'}, axis=1)
df['funding_rate_updated_dt'] = pd.to_datetime(df['funding_rate_updated_ts_ms'], unit='ms')
@@ -190,9 +193,6 @@ async def loop() -> None:
'net_mult_x_net_fr_abs','net_funding_rate_abs','net_funding_rate','next_funding_at_same_time','last_trade_ts_ast']
].sort_values(by='net_mult_x_net_fr_abs', ascending=False).reset_index(drop=True)
# min_daily_volume = 100_000
# df_best_fr_rate = df_best_fr_rate.loc[ (df_best_fr_rate['daily_volume_ast']>=min_daily_volume) & (df_best_fr_rate['daily_volume_ext']>min_daily_volume) ,:].reset_index(drop=True)
last_trade_max_ts = []
for index, row in df_best_fr_rate.iterrows():
r = json.loads(requests.get(f'https://api.starknet.extended.exchange/api/v1/info/markets/{row['symbol_ext']}/trades').text)
@@ -204,21 +204,41 @@ async def loop() -> None:
df_best_fr_rate['last_trade_ts_dt_ast'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ast'], unit='ms')
df_best_fr_rate['last_trade_ts_dt_ext'] = pd.to_datetime(df_best_fr_rate['last_trade_ts_ext'], unit='ms')
df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ast']) < (5*60*1000) ] # Last traded in 3min
# df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ext']) < (15*60*1000) ] # Last traded in 15min
# print(df_best_fr_rate.columns)
# print(df_best_fr_rate.iloc[0])
candles_ratios = []
for index, row in df_best_fr_rate.iterrows():
df = await get_candles(symbol=row['symbol_ext'])
try:
df = await get_candles(symbol=row['symbol_ext'])
except Exception as e:
logging.warning(f'BFR failed to get candles...sleeping and retrying: {e}')
time.sleep(5)
df = await get_candles(symbol=row['symbol_ext'])
buy_ratio_ext = float(df['med_ratio_aster_over_extend'].median())
buy_ratio_std = float(df['med_ratio_aster_over_extend'].std())
candles_ratios.append({'symbol_ext':row['symbol_ext'], 'buy_ratio_std': buy_ratio_std, 'buy_ratio_ext':buy_ratio_ext,'buy_ratio_ast':buy_ratio_ext*-1})
df_best_fr_rate = df_best_fr_rate.merge(pd.DataFrame(candles_ratios), on='symbol_ext', how='left')
### Set Unfiltered Master Data ###
master_data = df_best_fr_rate[
['symbol_ast','max_leverage_ast','lh_asset_ast','rh_asset_ast','funding_rate_ast','min_price_ast','min_order_size_ast','min_lot_size_ast','min_notional_ast','buy_ratio_ast',
'symbol_ext','max_leverage_ext','lh_asset_ext','rh_asset_ext','funding_rate_ext','min_price_ext','min_order_size_ext','min_lot_size_ext','min_notional_ext','buy_ratio_ext', 'buy_ratio_std','next_funding_at_same_time']
].to_json(orient='records')
VAL_KEY.set(name='fr_engine_best_fund_rate_master', value=str(master_data))
### Filter BFR Data ###
df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ast']) < (5*60*1000) ] # Last traded in 3min
df_best_fr_rate = df_best_fr_rate.loc[( (datetime.now().timestamp()*1000 )-df_best_fr_rate['last_trade_ts_ext']) < (15*60*1000) ] # Last traded in 15min
min_daily_volume = 100_000
df_best_fr_rate = df_best_fr_rate.loc[ (df_best_fr_rate['daily_volume_ast']>=min_daily_volume) & (df_best_fr_rate['daily_volume_ext']>min_daily_volume) ,:].reset_index(drop=True)
# print(df_best_fr_rate.columns)
# print(df_best_fr_rate.iloc[0])
if len(df_best_fr_rate) < 1:
raise ValueError(f'NO BFR RATE: {df_best_fr_rate}')
@@ -259,12 +279,7 @@ async def loop() -> None:
best_next_funding_pair: dict[str, dict] = {'ASTER': asdict(obj=ASTER), 'EXTEND': asdict(obj=EXTEND)}
VAL_KEY.set(name='fr_engine_best_fund_rate_output', value=json.dumps(obj=best_next_funding_pair))
master_data = df_best_fr_rate[
['symbol_ast','max_leverage_ast','lh_asset_ast','rh_asset_ast','funding_rate_ast','min_price_ast','min_order_size_ast','min_lot_size_ast','min_notional_ast','buy_ratio_ast',
'symbol_ext','max_leverage_ext','lh_asset_ext','rh_asset_ext','funding_rate_ext','min_price_ext','min_order_size_ext','min_lot_size_ext','min_notional_ext','buy_ratio_ext', 'buy_ratio_std','next_funding_at_same_time']
].to_json(orient='records')
VAL_KEY.set(name='fr_engine_best_fund_rate_master', value=str(master_data))
print(df_best_fr_rate[['symbol_ext','max_leverage_ext','buy_ratio_ext','net_funding_rate','daily_volume_ast','buy_ratio_ast']].head(10))
logging.info(f'BFR REFRESHED @ {datetime.now()}')
time.sleep(LOOP_SLEEP_SEC)