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Part Two: High frequency Trading – decoded

, August 21, 2013, 1 Comments

Data used By HFT Traders
Primarily HFT Traders Use High Speed TBT data at Colocation & for testing purpose they used Level 2 historical data.

How we Identify high-frequency traders ( Or HFT Trading Firms )
Mostly Prop shops with Co-locations Setup with better low latency hardwares / Low latency networking / Ultra Low latency OMS engines & more Interactive & TBT Links from exchange ( 400 messages links ) are fall under this category.
Or Trade records on exchanges include a Market Participant Identifier (MPID) indicating the broker/dealer making the trade. A broker/dealer may have multiple MPIDs that are used by different business lines or customers.

A typical reason for a customer to have their own MPID is that they have sponsored access ( Co-location ID with Market making expertise). In a typical sponsored access arrangement, the customer handles connectivity with an exchange and has limited interaction with the broker/dealer’s trading system. Sponsored access arrangements are motivated by exchanges’ tiered pricing schedules that give better pricing to higher-volume broker/dealers.

Customers accessing exchanges through sponsored access agreements get direct market access and the lower fees of the larger sponsoring broker/dealer. These sponsored access customers tend to be large, active traders or smaller broker/dealers. One consequence of these arrangements is that it is possible to observe activity of sponsored access customers directly rather than at the aggregated broker level.

The data from NSE classifies market participants as either a HFT or a non-HFT. Firms were classified as HFT firms using a variety of qualitative and quantitative criteria.

The firms ( Prop Shops with Co-location setup ) classified as HFTs typically use low-latency connections and trade more actively than other investors. Their orders have shorter durations than other investors, and they show a greater tendency to ip between long and short positions in a Market during a day.They are Volume driver’s.

How HFT Traders find the Asset class for its working.
A reasonable definition of the set of Asset classes traditional HFT Traders invest in is those in either most liquid products i.e Nifty 50 or bank Nifty or products those are not touch by most come Traders i.e Or Shares fall under those Index’s or they do Trading based Corporate news.

Or they do trading depends upon the Volatility on Asset classes – Small-cap stocks are slightly more volatile than mid and large-cap stocks. The standard deviation of small-cap stocks’ daily returns is 4.5%, compared to 3.5% for mid-cap stocks and 2.5% for large-cap stocks.

HFT’s are relatively more active in large-cap Futures Stock & Index’s. Their median share of total volume is 14:8% in small-cap stocks, 29:2% in mid-cap stocks, and 40:9% in large-cap stocks. It is conceivable that since HFT’s comparative advantage is reacting quickly to market events, they find more profit opportunities in Futures & Options for which quoted prices and depths update frequently.

How HFT Trades Take advantages of Trade imbalances.
There are two types of trade imbalances: marketable imbalances and buy- sell imbalances. The marketable imbalance is a common measure of buying and selling pressure .The buy-sell imbalance is simply shares bought minus shares sold and has been used to measure position changes of different investor groups

The simplest explanation of marketable imbalances is that they are essentially the number of shares bought with market orders minus the number of shares sold with market orders. There is actually no such thing as a market order in the NSE system, but any order to buy with a limit price at least as high as the best ask or order to sell with a limit price at least as low as the best bid is essentially the same thing as a market order.

The party submitting such an order is said to have submitted a marketable order, and the trade executes immediately. If the marketable order was a purchase, the trade can be said to be buyer-initiated, and if it is a sale, the trade can be said to be seller-initiated. Subtracting shares sold with marketable orders from shares bought with marketable orders gives the marketable imbalance measure. If investors with resting limit orders in the order book are passive liquidity suppliers, then the marketable imbalance is an intuitive measure of trading demand.

Intra-day returns for HFT traders
Intra-day returns are calculated using bid-ask midpoints from two sources. The primary source for quotes is the National Best Bid and Best Offer (NBBO). The NBBO aggregates quotes from all displayed order books.This is the best definition of a market price, but one disadvantage is that due to latency between exchanges and data feeds, the time stamp on the NBBO feed is not guaranteed to be synchronized with the time stamp for NSE trade data.

To ensure results are robust to misalignment of timestamps between the quote and trade data, I redo some results using the NSE best bid and best offer, or NSE BBO. The time stamp of quotes in the NSE BBO is precisely aligned with the time stamp of trade data. Creating the NSE BBO is computationally intensive, so I replicate only a subset of the results using the NSE BBO.

The quote feeds occasionally contain absurd price data. Every once in awhile, for a fraction of a second, the bid might be above the ask, or for a stock that typically trades at 125 Rs/, the best bid might be 0.25 Rs Tick . Returns calculated from such quotes do not accurately react changes in firm value. I use filters to screen out these prices. I remove quote updates where the bid is greater than the ask or where the bid-ask spread is more than 20% greater than the bid-ask midpoint. To fix an issue with bad pre-market quotes on the NSE, the last of which is used as a proxy for the opening price, I throw out the last price before the open if there is more than a 20% difference between the last pre-open bid-ask midpoint and the first post-open bid-ask midpoint.

After the application of these filters at the tick level, the global minimum and maximum one-second returns across all stock days, reported as -37% and 70%. While extreme, given the number of one-second observations in the sample, they do not seem like they would meaningfully affect the results. One could apply a stricter filter, such as requiring that the magnitude of one-second returns be less than 20%, but I do not think this would meaningfully affect results.( Data Used 16 august 2013 for 5 NSE Stocks)

HFT Traders are Liquidity provider – Yes/No ..
HFT can play an important role as market makers, for example, generating trading volume on new electronic exchanges .Trade volume, however, is not liquidity but all too often mistaken for it. Liquidity means “there is a bid/offer on the other side when I need it, for the amount I need it (market depth) at a reasonable level (market breadth). Volume is not the same as liquidity, since volume is approximately like the product of liquidity x velocity, and a large volume does not necessarily imply a large liquidity. This is illustrated by the May 6 flash crash when a fundamental trader’s algorithm started selling based on previous trade volume, creating a positive feedback between its own selling and the trading activity of other market participants.

The same event also demonstrated that HF Traders can turn into significant liquidity takers; while they are liquidity providers when it suits them (they have no obligation to make quotes). This is also described as “flow toxicity”, when market makers provide liquidity at their own loss or when informed traders take liquidity from uninformed traders. In fact it seems HFT provides liquidity in good times when it is perhaps least needed and takes liquidity away when it is most needed, thereby contributing rather than mitigating instability.

A recent report showed that the frantic development of HFT has slowed down in developed markets, and there is a transfer of activity to emerging markets such as Russia,India, Brazil and Mexico where exchanges are beginning to revamp their systems to attract such players. Low market volumes and stiff competition have led to a sharp fall in “high-frequency”. This illustrates the fact that, as HFT market participants flock into a given market, the opportunities shrink, dispelling the possibility for further growth.

It is also conceivable that HFT liquidity is provided at the expense of other market participants. Short term traders may be specifically prone to herd to the same information, driving the price further away from its fundamentals .The more momentum traders there are in a market and the higher the diversion from fundamentals, the fewer fundamental traders survive, further strengthening momentum traders. Various equilibrium are possible between short and long-term investors. The question is what is the right mix of investment strategies and horizons that best serves the well-functioning of financial markets and ultimately social welfare?

About author
Lokesh Madan is a strategy business consultant for various high frequency trading companies worldwide with more than 12 years of experience in financial technology, research work and business development ...more