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9

Using months of proprietary data that labels participants by their participant ID, it has been found that during periods of significant volatility, the composition of HFT participants in the book remains mostly constant as a fraction of the total BBO composition. What really changes, it was found, was that the fraction of low-frequency traders aggressing on ...


6

Financial modeling is often considered as a mixture of art and science. That is a way how you model your data depends on you. You can choose several approaches, for example: calculate max - min price for a given minute data - a very simple approach, calculate the standard deviation of minute-by-minute stock data, calculate GARCH family models for measuring ...


6

QuantConnect provides an open source, community driven project called Lean. The project has thousands of engineers using it to create event driven strategies, on any resolution data, any market or asset class. Our system models margin leverage and margin calls, cash limitations, transaction costs. We maintain a full cashbook of your currencies. Its about as ...


4

Well you have a few alternatives to lower your commissions. You can get your own broker number in which case you don't go through anyone, you go direct to the exchange so you just pay/get the active/passive rebate. If you are really HFT then this is often the route you take. For the case where you pay a commission to your broker, they are eating/taking ...


4

Quick summary: Your model should still be well specified, as long as: 1) You do the analysis on a heavily traded asset, e.g. IBM on NYSE, and 2) You use heteroskedasticity-consistent standard errors in your estimation framework, e.g. White's standard errors. I'm going to start the long answer by re-stating the question to make sure I've got it right. Let ...


2

In trading you need to make a lot of simple computation of a very large flow of data. FPGA are perfect that for. It is typically FPGA that will host marketfeed handler (see NOVASPARKS website, or ACCELLIZE) ; analytics computations ; risk computation (see ULLINK solution for instance). For more, this generic article is not that bad: Introducing ...


2

As a beginner in AlgoTrading QuantConnect and Quantopian are great for practice and improving your skills but for a serious Algo Trader , they are basically useless. An Algo Trader requires flexibility to investigate trading ideas and add or remove libraries or parts of the system that do not work. You need to automatically and constantly reevaluate your ...


2

I'll address your questions in order: 1a) For TSRV constructed using high frequency returns from NYSE market open to market close on a single day, the output should be numbers on the order of magnitude of 1e-4 to 1e-5. In other words, your numbers look about right. I got these number from calculating TSRV for IBM data myself using Kevin Sheppard's MatLab ...


2

It is all a matter of frequency. For instance if you want to get annual realized volatility you multiply your last expression by $\sqrt{(N*251)}$ or the second to last expression by $\sqrt{(251)}$. In other words, your last expression is the 5-min realized volatility whereas the second to last expression is the daily realized volatility.


2

I use Yhang Zhang measure for intraday volatility for timeseries with a rolling 5 or 10 day window. I wrote a C++ and vba implementation which I'm happy to share if you wish. Takes olhc data and gives an 'estimate' of the volatility. For intraday trading (gamma hedging), I found it is a fairly good estimator of the days range. But I would caution on whether ...


2

The classic text for machine learning is 'The Elements of Statistical Learning' by Tibshirani et al. I believe the term "data mining" is often used synonymously with "machine learning".


2

I did not know this provider, but had a look. for daily data, the url seems to be http://www.netfonds.no/quotes/paperhistory.php?paper=GOOG.O&csv_format=txt for market depth: http://www.netfonds.no/quotes/posdump.php?date=20160303&paper=E-SABL.BTSE&csv_format=txt for intraday trades: ...


2

Stock / ETF at 5-minute intervals can be downloaded from Yahoo Finance. See the code below: from urllib import urlretrieve import numpy as np, pandas as pd, sys import datetime as dt, requests import datetime, re, StringIO symbol = sys.argv[1] url='http://chartapi.finance.yahoo.com/instrument/1.0/%s/chartdata;type=quote;range=3d/csv' % symbol response = ...


2

For what concerns Forex data which is, however financial data after all, I often use http://www.histdata.com/. Their data is delivered in .CSV format. For timeframes, I quote the website: We can only deliver you time ordered Tick and M1 (1 minute) data. The data that we have available is organized by forex-pair/year/month. They also provide data for ...


1

Q1.) Is there anything wrong in principle with this simple sampling strategy? I mean sampling is a valid strategy, it just may not be the best. WOuld a VWAP style price be better? Would just an average be better? Typically when no trade has happened you can model the price as the last, average of the bid/ask spread, etc. The price you want depends ...


1

The code you posted is wrong since you do not model the time series behavior of the up/down process (ie if you have 10 up move and consequently 10 down move it is not the same as the opposite ie 10 down and after 10 up..). I would recommend you to use standards Arma Garch models apply on returns instead of modeling the process of up/down. These are (at ...


1

I second Tibshirani's book. There is an another edition you can download free on internet : http://www-bcf.usc.edu/~gareth/ISL/


1

There are many ways to calculate the volatility. timeframe does not metter. it can be monthly quarterly or daily data. You can call them as volatility metrics. Volatility Metrics Volatility is the degree of trading price over a specific time window. Historical volatility is the degree of price changes of past market prices.Volatility indicates the risk ...


1

By design, market makers do not exacerbate volatility because their trades are, as a whole, net passive.


1

http://bluemountaincapital.github.io/Deedle/ Disclaimer: I haven't used this.


1

From the Nasdaq page, IMBALANCE-ONLY CLOSE ORDERS Provides liquidity intended to offset on-close orders during the Closing Cross. Must be priced (limit), no market IO orders. IO buy/sell orders only execute at or above/below the 4:00 p.m., ET, bid/ask. They simply mean they were +\$0.01 or at \$23.56 from the price on their sell ...


1

I browsed through the work and this is what I see: the lhs $r_{t+1} + \cdots + r_{t+H}$ is the sum of log-returns after $t$. the rhs is indexed by $t-i, i=0, \ldots, H$ thus this has something to do with the past before (and at) $t$. Thus the regression models the future ($r_{t+1} + \cdots + r_{t+H}$) dependent of the past where only PCA projections of ...


1

HF data have a lot of auto correlation so common models to deal with this problems are ARFIMA, FIGARCH, Fractional Integrated GARCH. Engle recently propose the multiplicative components GARCH for high frequency data, which can include a mean model like and ARMA. In this post they explain how to implement it in R with the rugarch package, it takes some time ...


1

Exchanges provides the following six timestamps: Gateway In Timestamp-T1. Time at which the order was received by the Gateway from the members TCP connection. Gateway Out Timestamp-T2. This is the time when the order was dispatched by the Gateway to the Matching engine. Matcher In Timestamp-T3. This is the time the order was received by the Matching ...



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