Alex Morcos [ARCHIVE] on Nostr: 📅 Original date posted:2014-10-27 📝 Original message:I've been playing around ...
📅 Original date posted:2014-10-27
📝 Original message:I've been playing around with the code for estimating fees and found a few
issues with the existing code. I think this will address several
observations that the estimates returned by the existing code appear to be
too high. For instance see @cozz in Issue 4866
<https://github.com/bitcoin/bitcoin/issues/4866>;.
Here's what I found:
1) We're trying to answer the question of what fee X you need in order to
be confirmed within Y blocks. The existing code tries to do that by
calculating the median fee for each possible Y instead of gathering
statistics for each possible X. That approach is statistically incorrect.
In fact since certain X's appear so frequently, they tend to dominate the
statistics at all possible Y's (a fee rate of about 40k satoshis)
2) The existing code then sorts all of the data points in all of the
buckets together by fee rate and then reassigns buckets before calculating
the medians for each confirmation bucket. The sorting forces a
relationship where there might not be one. Imagine some other variable,
such as first 2 bytes of the transaction hash. If we sorted these and then
used them to give estimates, we'd see a clear but false relationship where
transactions with low starting bytes in their hashes took longer to confirm.
3) Transactions which don't have all their inputs available (because they
depend on other transactions in the mempool) aren't excluded from the
calculations. This skews the results.
I rewrote the code to follow a different approach. I divided all possible
fee rates up into fee rate buckets (I spaced these logarithmically). For
each transaction that was confirmed, I updated the appropriate fee rate
bucket with how many blocks it took to confirm that transaction.
The hardest part of doing this fee estimation is to decide what the
question really is that we're trying to answer. I took the approach that
if you are asking what fee rate I need to be confirmed within Y blocks,
then what you would like to know is the lowest fee rate such that a
relatively high percentage of transactions of that fee rate are confirmed
within Y blocks. Since even the highest fee transactions are confirmed
within the first block only 90-93% of the time, I decided to use 80% as my
cutoff. So now to answer "estimatefee Y", I scan through all of the fee
buckets from the most expensive down until I find the last bucket with >80%
of the transactions confirmed within Y blocks.
Unfortunately we still have the problem of not having enough data points
for non-typical fee rates, and so it requires gathering a lot of data to
give reasonable answers. To keep all of these data points in a circular
buffer and then sort them for every analysis (or after every new block) is
expensive. So instead I adopted the approach of keeping an exponentially
decaying moving average for each bucket. I used a decay of .998 which
represents a half life of 374 blocks or about 2.5 days. Also if a bucket
doesn't have very many transactions, I combine it with the next bucket.
Here is a link <https://github.com/morcos/bitcoin/pull/3>; to the code. I
can create an actual pull request if there is consensus that it makes sense
to do so.
I've attached a graph comparing the estimates produced for 1-3
confirmations by the new code and the old code. I did apply the patch to
fix issue 3 above to the old code first. The new code is in green and the
fixed code is in purple. The Y axis is a log scale of feerate in satoshis
per KB and the X axis is chain height. The new code produces the same
estimates for 2 and 3 confirmations (the answers are effectively quantized
by bucket).
I've also completely reworked smartfees.py. It turns out to require many
many more transactions are put through in order to have statistically
significant results, so the test is quite slow to run (about 3 mins on my
machine).
I've also been running a real world test, sending transactions of various
fee rates and seeing how long they took to get confirmed. After almost 200
tx's at each fee rate, here are the results so far:
Fee rate 1100 Avg blocks to confirm 2.30 NumBlocks:% confirmed 1: 0.528
2: 0.751 3: 0.870
Fee rate 2500 Avg blocks to confirm 2.22 NumBlocks:% confirmed 1: 0.528
2: 0.766 3: 0.880
Fee rate 5000 Avg blocks to confirm 1.93 NumBlocks:% confirmed 1: 0.528
2: 0.782 3: 0.891
Fee rate 10000 Avg blocks to confirm 1.67 NumBlocks:% confirmed 1: 0.569
2: 0.844 3: 0.943
Fee rate 20000 Avg blocks to confirm 1.33 NumBlocks:% confirmed 1: 0.715
2: 0.963 3: 0.989
Fee rate 30000 Avg blocks to confirm 1.27 NumBlocks:% confirmed 1: 0.751
2: 0.974 3: 1.0
Fee rate 40000 Avg blocks to confirm 1.25 NumBlocks:% confirmed 1: 0.792
2: 0.953 3: 0.994
Fee rate 60000 Avg blocks to confirm 1.12 NumBlocks:% confirmed 1: 0.875
2: 1.0 3: 1.0
Fee rate 100000 Avg blocks to confirm 1.09 NumBlocks:% confirmed 1: 0.901
2: 1.0 3: 1.0
Fee rate 300000 Avg blocks to confirm 1.12 NumBlocks:% confirmed 1: 0.886
2: 0.989 3: 1.0
Alex
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📝 Original message:I've been playing around with the code for estimating fees and found a few
issues with the existing code. I think this will address several
observations that the estimates returned by the existing code appear to be
too high. For instance see @cozz in Issue 4866
<https://github.com/bitcoin/bitcoin/issues/4866>;.
Here's what I found:
1) We're trying to answer the question of what fee X you need in order to
be confirmed within Y blocks. The existing code tries to do that by
calculating the median fee for each possible Y instead of gathering
statistics for each possible X. That approach is statistically incorrect.
In fact since certain X's appear so frequently, they tend to dominate the
statistics at all possible Y's (a fee rate of about 40k satoshis)
2) The existing code then sorts all of the data points in all of the
buckets together by fee rate and then reassigns buckets before calculating
the medians for each confirmation bucket. The sorting forces a
relationship where there might not be one. Imagine some other variable,
such as first 2 bytes of the transaction hash. If we sorted these and then
used them to give estimates, we'd see a clear but false relationship where
transactions with low starting bytes in their hashes took longer to confirm.
3) Transactions which don't have all their inputs available (because they
depend on other transactions in the mempool) aren't excluded from the
calculations. This skews the results.
I rewrote the code to follow a different approach. I divided all possible
fee rates up into fee rate buckets (I spaced these logarithmically). For
each transaction that was confirmed, I updated the appropriate fee rate
bucket with how many blocks it took to confirm that transaction.
The hardest part of doing this fee estimation is to decide what the
question really is that we're trying to answer. I took the approach that
if you are asking what fee rate I need to be confirmed within Y blocks,
then what you would like to know is the lowest fee rate such that a
relatively high percentage of transactions of that fee rate are confirmed
within Y blocks. Since even the highest fee transactions are confirmed
within the first block only 90-93% of the time, I decided to use 80% as my
cutoff. So now to answer "estimatefee Y", I scan through all of the fee
buckets from the most expensive down until I find the last bucket with >80%
of the transactions confirmed within Y blocks.
Unfortunately we still have the problem of not having enough data points
for non-typical fee rates, and so it requires gathering a lot of data to
give reasonable answers. To keep all of these data points in a circular
buffer and then sort them for every analysis (or after every new block) is
expensive. So instead I adopted the approach of keeping an exponentially
decaying moving average for each bucket. I used a decay of .998 which
represents a half life of 374 blocks or about 2.5 days. Also if a bucket
doesn't have very many transactions, I combine it with the next bucket.
Here is a link <https://github.com/morcos/bitcoin/pull/3>; to the code. I
can create an actual pull request if there is consensus that it makes sense
to do so.
I've attached a graph comparing the estimates produced for 1-3
confirmations by the new code and the old code. I did apply the patch to
fix issue 3 above to the old code first. The new code is in green and the
fixed code is in purple. The Y axis is a log scale of feerate in satoshis
per KB and the X axis is chain height. The new code produces the same
estimates for 2 and 3 confirmations (the answers are effectively quantized
by bucket).
I've also completely reworked smartfees.py. It turns out to require many
many more transactions are put through in order to have statistically
significant results, so the test is quite slow to run (about 3 mins on my
machine).
I've also been running a real world test, sending transactions of various
fee rates and seeing how long they took to get confirmed. After almost 200
tx's at each fee rate, here are the results so far:
Fee rate 1100 Avg blocks to confirm 2.30 NumBlocks:% confirmed 1: 0.528
2: 0.751 3: 0.870
Fee rate 2500 Avg blocks to confirm 2.22 NumBlocks:% confirmed 1: 0.528
2: 0.766 3: 0.880
Fee rate 5000 Avg blocks to confirm 1.93 NumBlocks:% confirmed 1: 0.528
2: 0.782 3: 0.891
Fee rate 10000 Avg blocks to confirm 1.67 NumBlocks:% confirmed 1: 0.569
2: 0.844 3: 0.943
Fee rate 20000 Avg blocks to confirm 1.33 NumBlocks:% confirmed 1: 0.715
2: 0.963 3: 0.989
Fee rate 30000 Avg blocks to confirm 1.27 NumBlocks:% confirmed 1: 0.751
2: 0.974 3: 1.0
Fee rate 40000 Avg blocks to confirm 1.25 NumBlocks:% confirmed 1: 0.792
2: 0.953 3: 0.994
Fee rate 60000 Avg blocks to confirm 1.12 NumBlocks:% confirmed 1: 0.875
2: 1.0 3: 1.0
Fee rate 100000 Avg blocks to confirm 1.09 NumBlocks:% confirmed 1: 0.901
2: 1.0 3: 1.0
Fee rate 300000 Avg blocks to confirm 1.12 NumBlocks:% confirmed 1: 0.886
2: 0.989 3: 1.0
Alex
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