AI News, Data science makes an impact on Wall Street
- On Monday, June 4, 2018
- By Read More
Data science makes an impact on Wall Street
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Having started my career in industry, working on problems in finance, I’ve always appreciated how challenging it is to build consistently profitable systems in this extremely competitive domain.
I quickly found that it was difficult to find and sustain profitable trading strategies that leveraged data sources that everyone else in the industry examined exhaustively.
In the early-to-mid 2000s the hedge fund industry began incorporating many more data sources, and today you’re likely to find many finance industry professionals at big data and data science events like Strata + Hadoop World.
Sentiment analysis is one product … Other more general market impact indicators are also in this area — topic clustering, topic classification, novelty detection;
Yes, publicly available news are news, but we also generate an enormous amount of our own content, we take in a number of different third-party contributor feeds, and we collect information from social media.
… The important part, really, in this case is not necessarily the set of techniques that work best, but how to pose the problem so that it is actually useful for finance professionals.
The actual challenge is asking the right question, then furthermore, doing enough in feature engineering and also enough statistics to convince ourselves and our clients that what is being produced actually makes sense, that it impacts financial markets in some way.
… If you use this consensus value, no matter how good it is, and you actually trade one of these instruments, there is inevitably going to be discrepancies between the traded price and the consensus price.
Among the Wall Street firms I’ve interacted with, Bloomberg ranks with the most active at evaluating new technologies and recruiting data scientists and data engineers experienced in using the latest technologies.
He also explained that they are beginning to help shape data science curriculums at several institutions and recruit graduates from many such programs: Physicists already tend to make for fairly good software engineers, especially people who do Monte Carlo simulations or do particle physics because you have to work with a lot of data.
Python For Finance: Algorithmic Trading
As you have seen in the introduction, this data clearly contains the four columns with the opening and closing price per day and the extreme high and low price movements for the Apple stock for each day.
The latter, on the other hand, is the adjusted closing price: it’s the closing price of the day that has been slightly adjusted to include any actions that occurred at any time before the next day’s open.
This is extremely handy in cases where, for example, the Yahoo API endpoint has changed and you don’t have access to your data any longer :) Now that you have briefly inspected the first lines of your data and have taken a look at some summary statistics, it’s time to go a little bit deeper.
To conclude, assign the latter to a variable ts and then check what type ts is by using the type() function: The square brackets can be nice to subset your data, but they are maybe not the most idiomatic way to do things with Pandas.
The resample() function is often used because it provides elaborate control and more flexibility on the frequency conversion of your times series: besides specifying new time intervals yourself and specifying how you want to handle missing data, you also have the option to indicate how you want to resample your data, as you can see in the code example above.
Lastly, before you take your data exploration to the next level and start with visualizing your data and performing some common financial analyses on your data, you might already start to calculate the differences between the opening and closing prices per day.
You storethe result in a new column of the aapl DataFrame called diff and then you delete it again with the help of del: Tip: make sure to comment out the last line of code so that the new column of your aapl DataFrame doesn’t get removed and you can check the results of your arithmetic operation!
Of course, knowing the gains in absolute terms might already help you to get an idea of whether you’re making a good investment, but as a quant, you might be more interested in a more relative means of measuring your stock’s value, like how much the value of a certain stock has gone up or gone down.
Before you finance or lease a car, look at your financial situation to make sure you have enough income to cover your monthly living expenses.
Saving for a down payment or trading in a car can reduce the amount you need to finance or lease, which then lowers your financing or leasing costs.
If you owe more than the car is worth, that’s called negative equity, which can affect the financing of your new car or the lease agreement.
It’s a good idea to check your credit report and credit score when you are considering financing or leasing a car, and before you make any major purchase.
Contact any of the three nationwide credit reporting agencies: Usually, you will get your credit score after you apply for financing or a lease.
For more information about credit reports and credit scores, see: If you don’t have a credit history – or a strong credit history – a creditor may require that you have a co-signer on the finance contract or lease agreement.
The account payment history will appear on your credit report and the co-signer’s – which means late payments will hurt both of your credit.
You and a dealer enter into a contract where you buy a car and also agree to pay, over a period of time, the amount financed plus a finance charge.
The dealer typically sells the contract to a bank, finance company or credit union that services the account and collects your payments.
Know that the total amount you will pay will depend on several factors, including: Many creditors now offer longer-term credit, such as 72 or 84 months to pay.
It is important to compare different payment plans for both the monthly payment and total of payments required, for example, for a 48-month/4-year and a 60-month/5-year credit purchase.
In general, longer contract lengths mean lower monthly payments, higher total finance charges, and higher overall costs.
The F&I Department manager will ask you to complete a credit application, which may include your: Most dealerships will get a copy of your credit report, which has information about your current and past credit, your payment record, and data from public records (like a bankruptcy filing from court documents).
That means you’re paying for the car’s expected depreciation during the lease period, plus a rent charge, taxes, and fees.
You can negotiate a higher mileage limit, but that normally increases the monthly payment, because the car depreciates more during the life of the lease.
If you go beyond the mileage limit in the lease agreement, you probably will have to pay an additional charge when you return the car.
You also must service the car according to the manufacturer’s recommendations and maintain insurance that meets the leasing company’s standards.
Federal law lets you terminate the lease with no early termination charges IF: Other fees may still apply, including those for excess wear, use, and mileage.
Be sure you have a copy of the credit contract or lease agreement, with all signatures and terms filled in, before you leave the dealership.
The creditor may repossess the car or may sell the car and apply the proceeds from the sale to the outstanding balance on your credit agreement.
For more information, including definitions of common terms used when financing or leasing a car, read “Understanding Vehicle Financing,” jointly prepared by the American Financial Services Association Education Foundation, the National Automobile Dealers Association, and the FTC.
Banks look to the stars to spot trading mistakes
The machine learning technology that safeguards Europe’s deep-space missions could soon be used to reduce the risk of “fat finger” trades, after the European Space Agency agreed its first collaboration with the financial services industry. The ESA is working with financial analytics business Mosaic Smart Data to find other uses for algorithms that keep satellites running safely by monitoring and analysing their tens of thousands of instruments for early signs of problems. Mosaic Smart Data hopes the same underlying algorithms can be used to monitor and analyse millions of data points attached to financial trading, so mistakes and fraud can be caught sooner.
“Banks rely on data from previous misdoings [to spot outliers],” he said. “There are too few of those instances to build highly robust models and such models and such analysis often relies on quite inflexible rules-based approaches, like if a threshold is breached it triggers an alert.” The ESA models, in contrast, are based on surveillance of current market activity and can be used to analyse both the banks’ internal data, third-party market and economic data and other factors. “These models have a far greater level of subtlety,” said Mr Hodgson, and are able to detect “behaviour outside the norm even if it is within a given threshold”. A
Algorithmic trading is a method of executing a large order (too large to fill all at once) using automated pre-programmed trading instructions accounting for variables such as time, price, and volume to send small slices of the order (child orders) out to the market over time.
Many fall into the category of high-frequency trading (HFT), which are characterized by high turnover and high order-to-trade ratios. As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe.
Profitability projections by the TABB Group, a financial services industry research firm, for the US equities HFT industry were US$1.3 billion before expenses for 2014, significantly down on the maximum of US$21 billion that the 300 securities firms and hedge funds that then specialized in this type of trading took in profits in 2008, which the authors had then called 'relatively small' and 'surprisingly modest' when compared to the market's overall trading volume.
In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years the firm as a whole was profitable on 1,277 out of 1,278 trading days, losing money just one day, empirically demonstrating the law of large numbers benefit of trading thousands to millions of tiny, low-risk and low-edge trades every trading day. A
third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms. As of 2009, studies suggested HFT firms accounted for 60–73% of all US equity trading volume, with that number falling to approximately 50% in 2012. In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007.
Foreign exchange markets also have active algorithmic trading (about 25% of orders in 2006). Futures markets are considered fairly easy to integrate into algorithmic trading, with about 20% of options volume expected to be computer-generated by 2010.[needs update] Bond markets are moving toward more access to algorithmic traders. Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010 Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility by rapidly pulling liquidity from the market.
(See List of largest daily changes in the Dow Jones Industrial Average.) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while 'algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010.' However, other researchers have reached a different conclusion.
One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors. Computerization of the order flow in financial markets began in the early 1970s, with some landmarks being the introduction of the New York Stock Exchange's “designated order turnaround” system (DOT, and later SuperDOT), which routed orders electronically to the proper trading post, which executed them manually.
Most retirement savings, such as private pension funds or 401(k) and individual retirement accounts in the US, are invested in mutual funds, the most popular of which are index funds which must periodically 'rebalance' or adjust their portfolio to match the new prices and market capitalization of the underlying securities in the stock or other index that they track. Profits are transferred from passive index investors to active investors, some of whom are algorithmic traders specifically exploiting the index rebalance effect.
The magnitude of these losses incurred by passive investors has been estimated at 21-28bp per year for the S&P 500 and 38-77bp per year for the Russell 2000. John Montgomery of Bridgeway Capital Management says that the resulting 'poor investor returns' from trading ahead of mutual funds is 'the elephant in the room' that 'shockingly, people are not talking about.' Pairs trading or pair trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative value of close substitutes.
Modern algorithms are often optimally constructed via either static or dynamic programming .   Recently, HFT, which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial. These algorithms or techniques are commonly given names such as 'Stealth' (developed by the Deutsche Bank), 'Iceberg', 'Dagger', 'Guerrilla', 'Sniper', 'BASOR' (developed by Quod Financial) and 'Sniffer'. Dark pools are alternative trading systems that are private in nature—and thus do not interact with public order flow—and seek instead to provide undisplayed liquidity to large blocks of securities. In dark pools trading takes place anonymously, with most orders hidden or 'iceberged.' Gamers or 'sharks' sniff out large orders by 'pinging' small market orders to buy and sell.
Steps taken to reduce the chance of over optimization can include modifying the inputs +/- 10%, schmooing the inputs in large steps, running monte carlo simulations and ensuring slippage and commission is accounted for. Forward testing the algorithm is the next stage and involves running the algorithm through an out of sample data set to ensure the algorithm performs within backtested expectations.
Although there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, specialized order types, co-location, very short-term investment horizons, and high cancellation rates for orders. In the U.S., high-frequency trading (HFT) firms represent 2% of the approximately 20,000 firms operating today, but account for 73% of all equity trading volume. As of the first quarter in 2009, total assets under management for hedge funds with HFT strategies were US$141 billion, down about 21% from their high. The HFT strategy was first made successful by Renaissance Technologies. High-frequency funds started to become especially popular in 2007 and 2008. Many HFT firms are market makers and provide liquidity to the market, which has lowered volatility and helped narrow Bid-offer spreads making trading and investing cheaper for other market participants. HFT has been a subject of intense public focus since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission stated that both algorithmic trading and HFT contributed to volatility in the 2010 Flash Crash.
Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange. Another set of HFT strategies in classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency.
subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc., to change the price or rate relationship of two or more financial instruments and permit the arbitrageur to earn a profit. Merger arbitrage also called risk arbitrage would be an example of this.
Quote stuffing is a tactic employed by malicious traders that involves quickly entering and withdrawing large quantities of orders in an attempt to flood the market, thereby gaining an advantage over slower market participants. The rapidly placed and canceled orders cause market data feeds that ordinary investors rely on to delay price quotes while the stuffing is occurring.
Researchers showed high-frequency traders are able to profit by the artificially induced latencies and arbitrage opportunities that result from quote stuffing. Network-induced latency, a synonym for delay, measured in one-way delay or round-trip time, is normally defined as how much time it takes for a data packet to travel from one point to another. Low latency trading refers to the algorithmic trading systems and network routes used by financial institutions connecting to stock exchanges and electronic communication networks (ECNs) to rapidly execute financial transactions. Most HFT firms depend on low latency execution of their trading strategies.
Joel Hasbrouck and Gideon Saar (2013) measure latency based on three components: the time it takes for 1) information to reach the trader, 2) the trader’s algorithms to analyze the information, and 3) the generated action to reach the exchange and get implemented. In a contemporary electronic market (circa 2009), low latency trade processing time was qualified as under 10 milliseconds, and ultra-low latency as under 1 millisecond. Low-latency traders depend on ultra-low latency networks.
They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform to benefit from implementing high-frequency strategies. Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors.
Finance is essentially becoming an industry where machines and humans share the dominant roles – transforming modern finance into what one scholar has called, “cyborg finance.” While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading.
Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story. 'Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way,' as they constantly seek out new trading advantages said Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group.
Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics. 'Markets are by their very nature conversations, having grown out of coffee houses and taverns,' he said.
'More of our customers are finding ways to use news content to make money.' An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of the Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by two seconds in reporting an interest rate cut by the Bank of England.
In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets, led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review.
For example, in June 2007, the London Stock Exchange launched a new system called TradElect that promises an average 10 millisecond turnaround time from placing an order to final confirmation and can process 3,000 orders per second. Since then, competitive exchanges have continued to reduce latency with turnaround times of 3 milliseconds available.
- On Tuesday, December 10, 2019
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