Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

AutoFees Group By Pubkeys #405

Open
wants to merge 3 commits into
base: v1.10.0
Choose a base branch
from
Open

AutoFees Group By Pubkeys #405

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
d6cd83e
group AF updates by pubkey
cryptosharks131 Nov 27, 2024
fcf2190
fix merge issues
cryptosharks131 Nov 27, 2024
3a7a6f5
empty forwards condition
cryptosharks131 Nov 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
29 changes: 19 additions & 10 deletions af.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
from django.db.models import Sum
from datetime import datetime, timedelta
from os import environ
from pandas import DataFrame, concat
from pandas import DataFrame, concat, merge
environ['DJANGO_SETTINGS_MODULE'] = 'lndg.settings'
django.setup()
from gui.models import Forwards, Channels, LocalSettings, FailedHTLCs
Expand Down Expand Up @@ -62,7 +62,9 @@ def main(channels):
forwards_df_in_1d_sum = DataFrame.from_records(forwards_1d.values('chan_id_in').annotate(amt_out_msat=Sum('amt_out_msat'), fee=Sum('fee')), 'chan_id_in')
if forwards.exists():
forwards_df_in_7d_sum = DataFrame.from_records(forwards.values('chan_id_in').annotate(amt_out_msat=Sum('amt_out_msat'), fee=Sum('fee')), 'chan_id_in')
forwards_df_in_7d_sum = merge(forwards_df_in_7d_sum, channels_df[['chan_id', 'remote_pubkey']], left_on='chan_id_in', right_on='chan_id', how='left')
forwards_df_out_7d_sum = DataFrame.from_records(forwards.values('chan_id_out').annotate(amt_out_msat=Sum('amt_out_msat'), fee=Sum('fee')), 'chan_id_out')
forwards_df_out_7d_sum = merge(forwards_df_out_7d_sum, channels_df[['chan_id', 'remote_pubkey']], left_on='chan_id_out', right_on='chan_id', how='left')
else:
forwards_df_in_7d_sum = DataFrame()
forwards_df_out_7d_sum = DataFrame()
Expand All @@ -76,29 +78,34 @@ def main(channels):
channels_df['net_routed_7day'] = channels_df.apply(lambda row: round((row['amt_routed_out_7day']-row['amt_routed_in_7day'])/row['capacity'], 1), axis=1)
channels_df['local_balance'] = channels_df.apply(lambda row: row.local_balance + row.pending_outbound, axis=1)
channels_df['remote_balance'] = channels_df.apply(lambda row: row.remote_balance + row.pending_inbound, axis=1)
channels_df['in_percent'] = channels_df.apply(lambda row: int(round((row['remote_balance']/row['capacity'])*100, 0)), axis=1)
channels_df['out_percent'] = channels_df.apply(lambda row: int(round((row['local_balance']/row['capacity'])*100, 0)), axis=1)
channels_df['eligible'] = channels_df.apply(lambda row: (datetime.now()-row['fees_updated']).total_seconds() > (update_hours*3600), axis=1)

# Records for AutoFees to process, grouped by peer pubkey
autofees_df = channels_df[['remote_pubkey', 'capacity', 'local_balance', 'remote_balance', 'local_fee_rate', 'amt_routed_in_1day', 'amt_routed_in_7day', 'amt_routed_out_7day', 'net_routed_7day']].copy()
autofees_df = autofees_df.groupby('remote_pubkey').sum()
autofees_df['in_percent'] = autofees_df.apply(lambda row: int(round((row['remote_balance']/row['capacity'])*100, 0)), axis=1)
autofees_df['out_percent'] = autofees_df.apply(lambda row: int(round((row['local_balance']/row['capacity'])*100, 0)), axis=1)

# Low Liquidity
lowliq_df = channels_df[channels_df['out_percent'] <= lowliq_limit].copy()
lowliq_df = autofees_df[autofees_df['out_percent'] <= lowliq_limit].copy()
failed_htlc_df = DataFrame.from_records(FailedHTLCs.objects.exclude(wire_failure=99).filter(timestamp__gte=filter_1day).order_by('-id').values())
if failed_htlc_df.shape[0] > 0:
failed_htlc_df = failed_htlc_df[(failed_htlc_df['wire_failure']==15) & (failed_htlc_df['failure_detail']==6) & (failed_htlc_df['amount']>failed_htlc_df['chan_out_liq']+failed_htlc_df['chan_out_pending'])]
lowliq_df['failed_out_1day'] = 0 if failed_htlc_df.empty else lowliq_df.apply(lambda row: len(failed_htlc_df[failed_htlc_df['chan_id_out']==row.chan_id]), axis=1)
failed_htlc_df = merge(failed_htlc_df, channels_df[['chan_id', 'remote_pubkey']], left_on='chan_id_out', right_on='chan_id', how='left')
lowliq_df['failed_out_1day'] = 0 if failed_htlc_df.empty else merge(lowliq_df, failed_htlc_df[['remote_pubkey']], left_index=True, right_on='remote_pubkey', how='left').groupby('remote_pubkey').size().reindex(lowliq_df.index, fill_value=0)
# INCREASE IF (failed htlc > threshhold) && (flow in == 0)
lowliq_df['new_rate'] = lowliq_df.apply(lambda row: row['local_fee_rate']+(5*multiplier) if row['failed_out_1day']>failed_htlc_limit and row['amt_routed_in_1day'] == 0 else row['local_fee_rate'], axis=1)

# Balanced Liquidity
balanced_df = channels_df[(channels_df['out_percent'] > lowliq_limit) & (channels_df['out_percent'] < excess_limit)].copy()
balanced_df = autofees_df[(autofees_df['out_percent'] > lowliq_limit) & (autofees_df['out_percent'] < excess_limit)].copy()
# IF NO FLOW THEN DECREASE FEE AND IF HIGH FLOW THEN SLOWLY INCREASE FEE
balanced_df['new_rate'] = balanced_df.apply(lambda row: row['local_fee_rate']+((2*multiplier)*(1+(row['net_routed_7day']/row['capacity']))) if row['net_routed_7day'] > 1 else row['local_fee_rate'], axis=1)
balanced_df['new_rate'] = balanced_df.apply(lambda row: row['local_fee_rate']-(3*multiplier) if (row['amt_routed_in_7day']+row['amt_routed_out_7day']) == 0 else row['new_rate'], axis=1)

# Excess Liquidity
excess_df = channels_df[channels_df['out_percent'] >= excess_limit].copy()
excess_df['revenue_7day'] = excess_df.apply(lambda row: int(forwards_df_out_7d_sum.loc[row.chan_id].fee) if forwards_df_out_7d_sum.empty == False and (forwards_df_out_7d_sum.index == row.chan_id).any() else 0, axis=1)
excess_df['revenue_assist_7day'] = excess_df.apply(lambda row: int(forwards_df_in_7d_sum.loc[row.chan_id].fee) if forwards_df_in_7d_sum.empty == False and (forwards_df_in_7d_sum.index == row.chan_id).any() else 0, axis=1)
excess_df = autofees_df[autofees_df['out_percent'] >= excess_limit].copy()
excess_df['revenue_7day'] = 0 if forwards_df_out_7d_sum.empty else merge(excess_df, forwards_df_out_7d_sum.groupby('remote_pubkey').sum('fee')[['fee']], on='remote_pubkey', how='left')['fee'].fillna(0).astype(int)
excess_df['revenue_assist_7day'] = 0 if forwards_df_in_7d_sum.empty else merge(excess_df, forwards_df_in_7d_sum.groupby('remote_pubkey').sum('fee')[['fee']], on='remote_pubkey', how='left')['fee'].fillna(0).astype(int)
# DECREASE IF (assisting channel or stagnant liq)
excess_df['new_rate'] = excess_df.apply(lambda row: row['local_fee_rate']-(5*multiplier) if row['net_routed_7day'] < 0 and row['revenue_assist_7day'] > (row['revenue_7day']*10) else row['local_fee_rate'], axis=1)
excess_df['new_rate'] = excess_df.apply(lambda row: row['local_fee_rate']-(5*multiplier) if (row['amt_routed_in_7day']+row['amt_routed_out_7day']) == 0 else row['new_rate'], axis=1)
Expand All @@ -109,7 +116,9 @@ def main(channels):
result_df['new_rate'] = result_df.apply(lambda row: max_rate if max_rate < row['new_rate'] else row['new_rate'], axis=1)
result_df['new_rate'] = result_df.apply(lambda row: min_rate if min_rate > row['new_rate'] else row['new_rate'], axis=1)
result_df['adjustment'] = result_df.apply(lambda row: int(row['new_rate']-row['local_fee_rate']), axis=1)
return result_df
channels_df = merge(channels_df, result_df[['new_rate', 'adjustment']], on='remote_pubkey', how='left')

return channels_df
else:
return DataFrame()

Expand Down