From mboxrd@z Thu Jan 1 00:00:00 1970 Received: from sog-mx-2.v43.ch3.sourceforge.com ([172.29.43.192] helo=mx.sourceforge.net) by sfs-ml-2.v29.ch3.sourceforge.com with esmtp (Exim 4.76) (envelope-from ) id 1Xiq37-0004Qg-63 for bitcoin-development@lists.sourceforge.net; Mon, 27 Oct 2014 19:33:53 +0000 Received-SPF: pass (sog-mx-2.v43.ch3.sourceforge.com: domain of gmail.com designates 209.85.223.179 as permitted sender) client-ip=209.85.223.179; envelope-from=morcos@gmail.com; helo=mail-ie0-f179.google.com; Received: from mail-ie0-f179.google.com ([209.85.223.179]) by sog-mx-2.v43.ch3.sourceforge.com with esmtps (TLSv1:RC4-SHA:128) (Exim 4.76) id 1Xiq35-00011s-Dv for bitcoin-development@lists.sourceforge.net; Mon, 27 Oct 2014 19:33:53 +0000 Received: by mail-ie0-f179.google.com with SMTP id ar1so5092151iec.38 for ; Mon, 27 Oct 2014 12:33:45 -0700 (PDT) MIME-Version: 1.0 X-Received: by 10.107.40.136 with SMTP id o130mr25792068ioo.26.1414438425417; Mon, 27 Oct 2014 12:33:45 -0700 (PDT) Received: by 10.50.223.146 with HTTP; Mon, 27 Oct 2014 12:33:45 -0700 (PDT) Date: Mon, 27 Oct 2014 15:33:45 -0400 Message-ID: From: Alex Morcos To: bitcoin-development@lists.sourceforge.net Content-Type: multipart/mixed; boundary=001a1141e914e7ace405066c9b4a X-Spam-Score: -0.6 (/) X-Spam-Report: Spam Filtering performed by mx.sourceforge.net. See http://spamassassin.org/tag/ for more details. -1.5 SPF_CHECK_PASS SPF reports sender host as permitted sender for sender-domain 0.0 FREEMAIL_FROM Sender email is commonly abused enduser mail provider (morcos[at]gmail.com) -0.0 SPF_PASS SPF: sender matches SPF record 1.0 HTML_MESSAGE BODY: HTML included in message -0.1 DKIM_VALID_AU Message has a valid DKIM or DK signature from author's domain 0.1 DKIM_SIGNED Message has a DKIM or DK signature, not necessarily valid -0.1 DKIM_VALID Message has at least one valid DKIM or DK signature X-Headers-End: 1Xiq35-00011s-Dv Subject: [Bitcoin-development] Reworking the policy estimation code (fee estimates) X-BeenThere: bitcoin-development@lists.sourceforge.net X-Mailman-Version: 2.1.9 Precedence: list List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Mon, 27 Oct 2014 19:33:53 -0000 --001a1141e914e7ace405066c9b4a Content-Type: multipart/alternative; boundary=001a1141e914e7acdf05066c9b48 --001a1141e914e7acdf05066c9b48 Content-Type: text/plain; charset=UTF-8 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 . 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 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 --001a1141e914e7acdf05066c9b48 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
I've been playing around with the code for estimating = fees and found a few issues with the existing code. =C2=A0 I think this wil= l address several observations that the estimates returned by the existing = code appear to be too high.=C2=A0 For instance see @cozz in Issue 4866.

<= div>Here's what I found:

1)=C2=A0We're trying to answer the question of what fee X y= ou need in order to be confirmed within Y blocks. =C2=A0 The existing code = tries to do that by calculating the median fee for each possible Y instead = of gathering statistics for each possible X.=C2=A0 That approach is=C2=A0st= atistically=C2=A0incorrect.=C2=A0 In fact since certain X's appear so f= requently, they tend to dominate the statistics at all possible Y's (a = fee rate of about 40k satoshis)

2) The existing code then sorts al= l of the data points in all of the buckets together by fee rate and then re= assigns buckets before calculating the medians for each confirmation bucket= . =C2=A0= The sorting forces a relationship where there might not be one.=C2=A0 Imagi= ne some other variable, such as first 2 bytes of the transaction hash.=C2= =A0 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&= #39;t have all their inputs available (because they depend on other transac= tions in the mempool) aren't excluded from the calculations.=C2=A0 This= skews the results.

I rewrote the code to follow a different approach.=C2=A0 I divide= d all possible fee rates up into fee rate buckets (I spaced these logarithm= ically).=C2=A0 For each transaction that was confirmed, I updated the appro= priate fee rate bucket=C2=A0with how many blocks it took to confirm that tr= ansaction. =C2=A0

The hardest part of doing this fee estimation is= to decide what the question really is that we're trying to answer.=C2= =A0 I took the approach that if you are asking what fee rate I need to be c= onfirmed within Y blocks, then what you would like to know is the lowest fe= e rate such that a relatively high percentage of transactions of that fee r= ate 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.=C2=A0 So now to answer "estimatefee Y", I s= can through all of the fee buckets from the most expensive down until I fin= d the last bucket with >80% of the transactions confirmed within Y block= s.

Unfortunately we still have the problem of not having e= nough 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 i= n a circular buffer and then sort them for every analysis (or after every n= ew block) is expensive.=C2=A0 So instead I adopted the approach of keeping = an exponentially decaying moving average for each bucket. =C2=A0I used a decay of .= 998 which represents a half life of 374 blocks or about 2.5 days. =C2=A0Also if a b= ucket doesn't have very many transactions, I combine it with the next b= ucket.

Here is a = link to the code.=C2=A0 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.=C2=A0 I did apply the patch to fix issue 3 above to the old = code first.=C2=A0 The new code is in green and the fixed code is in purple.= =C2=A0 The Y axis is a log scale of feerate in satoshis per KB and the X ax= is is chain height.=C2=A0 The new code produces the same estimates for 2 an= d 3 confirmations (the answers are effectively quantized by bucket).=

=
I've also completely reworked smartfees.py.=C2=A0 It turns out to re= quire many many more transactions are put through in order to have statisti= cally significant results, so the test is quite slow to run (about 3 mins o= n my machine).

I've also been running a real world test, sendi= ng transactions of various fee rates and seeing how long they took to get c= onfirmed.=C2=A0 After almost 200 tx's at each fee rate, here are the re= sults so far:

Fee rate 1100 =C2=A0 Avg blocks to confirm 2.30 NumBlocks:% c= onfirmed 1: 0.528 2: 0.751 3: 0.870
Fee rate 2500 =C2=A0 Avg blocks to confirm 2.22 NumBlocks:%= confirmed 1: 0.528 2: 0.766 3: 0.880
Fee rate 5000 =C2=A0 Avg blocks to confirm 1.93 NumBlocks= :% confirmed 1: 0.528 2: 0.782 3: 0.891
Fee rate 10000 =C2=A0Avg blocks to confirm 1.67 NumBloc= ks:% confirmed 1: 0.569 2: 0.844 3: 0.943
Fee rate 20000 =C2=A0Avg blocks to confirm 1.33 NumBl= ocks:% confirmed 1: 0.715 2: 0.963 3: 0.989
Fee rate 30000 =C2=A0Avg blocks to confirm 1.27 Num= Blocks:% confirmed 1: 0.751 2: 0.974 3: 1.0
Fee rate 40000 =C2=A0Avg blocks to confirm 1.25 Num= Blocks:% confirmed 1: 0.792 2: 0.953 3: 0.994
Fee rate 60000 =C2=A0Avg blocks to confirm 1.12= NumBlocks:% confirmed 1: 0.875 2: 1.0 =C2=A0 3: 1.0
Fee rate 100000 Avg blocks to confirm 1.09= NumBlocks:% confirmed 1: 0.901 2: 1.0 =C2=A0 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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