I am trying to loop over a DataFrame.
my code sample is the following:
for index1, long in tqdm(sim_df_ha_rsi.iterrows(), total=sim_df_ha_rsi.shape[0]):
if index1 in validLongLimits.index:
order_index += 1
orders[index] = {
'Type': 'LONG',
'Open': open_price,
'DateOpen': start_date,
'Commission_open': open_price * 0.0001 * amount,
'Commission_close': None,
'Close': None,
'DateClose': None,
'Profit': None,
'Balance': 165000,
'IsOpen': True
}
for index2, short in sim_df_ha_rsi.iloc[index1:].iterrows():
if index2 in validShortLimits.index:
if long['Date'] < short['Date']:
orders[index]['IsOpen'] = False
orders[index]['DateClose'] = short['Date']
orders[index]['Close'] = short['Close']
orders[index]['Commission_close'] = short['Close'] * 0.0001 * amount
for index3, stop in sim_df_ha_rsi.iloc[index2:].iterrows():
if index3 in validLongLimits.index:
open_price = validLongLimits.loc[ validLongLimits.index == index3 ]['Open'].values[0]
start_date = validLongLimits.loc[ validLongLimits.index == index3 ]['Date'].values[0]
index = index3
break
break
I fail to catch the following pattern:
let sim_df_ha_rsi be the data. validLongLimits and validShortLimits are sublists of the data. for a given value in validLongLimits, find the following value which belongs to validShortLimits, then reloop for the next value in validLongLimits which comes after the shorted one.
sim_df_ha_rsi is like:
Date Open High Low Close RSI_14_1m RSI_14_5m
0 2023-01-03 10:45:00 20.090000 20.180000 20.059999 20.115000 NaN NaN
1 2023-01-03 11:00:00 20.110001 20.100000 19.870001 19.990000 NaN NaN
2 2023-01-03 11:15:00 19.995000 19.950001 19.860001 19.897500 NaN NaN
3 2023-01-03 11:30:00 19.889999 19.889999 19.799999 19.852499 NaN NaN
4 2023-01-03 11:45:00 19.860000 19.850000 19.790001 19.817500 NaN NaN
... ... ... ... ... ... ... ...
313 2023-01-17 17:00:00 17.675000 17.750000 17.650000 17.700000 72.283634 62.390653
314 2023-01-17 17:15:00 17.700000 17.740000 17.629999 17.682499 70.135453 62.390653
315 2023-01-17 17:30:00 17.679999 17.660000 17.370001 17.547500 56.248421 55.891129
316 2023-01-17 17:45:00 17.580000 17.540001 17.410000 17.487500 51.379162 55.891129
317 2023-01-17 18:00:00 17.500000 17.520000 17.520000 17.520000 53.716387 55.891129
my output would be like:
Type Open DateOpen Commission_open Commission_close Close DateClose Profit Balance IsOpen
63 LONG 19.430000 2023-01-05 11:30:00 0.19430 None None None None 165000 True
74 LONG 19.600000 2023-01-05 14:15:00 0.19600 None None None None 165000 True
75 LONG 19.420000 2023-01-05 14:30:00 0.19420 None None None None 165000 True
76 LONG 19.330000 2023-01-05 14:45:00 0.19330 None None None None 165000 True
I want to open position and close position consecutively through data with specific conditions. Then, recalculate it with the price I closed the previous one.
my code seemingly works but shortcuts many gaps. It makes many calculations to return, say, 2 elements. breaks cause some fragility in the code. I don't want to use any break statement but don't know how to do.
Would a while loop with an anchor be more useful here?
Paraphase your inner for-loop line by line (but haven't tested with data):
short_df = sim_df_ha_rsi.iloc[index1:].filter(items=validShortLimits.index, axis=0)
short_df = short_df[short_df['Date'] > long['Date']]
if len(short_df) > 0:
short = short_df.iloc[0]
index2 = short.name
orders[index]['IsOpen'] = False
orders[index]['DateClose'] = short['Date']
orders[index]['Close'] = short['Close']
orders[index]['Commission_close'] = short['Close'] * 0.0001 * amount
long_df = sim_df_ha_rsi.iloc[index2:].filter(items=validLongLimits.index, axis=0)
if len(long_df) > 0:
open_price = long_df.iloc[0]['Open'].values[0]
start_date = long_df.iloc[0]['Date'].values[0]
index = long_df.iloc[0].name