Options trading python
Having accurate volatility is essential. In this post, I will try and cover what I have learnt about Options Trading in the due course.
Who Uses Algorithmic Trading Software?
Whether buying or building, the trading software should have a high degree of customization and configurability. Functionality to Write Custom Programs. Most trading software sold by the third-party vendors offers the ability to write your own custom programs within it. This allows a trader to experiment and try any trading concept he or she develops. Software that offers coding in the programming language of your choice is obviously preferred.
For more, see " Trading Systems Coding. Backtesting Feature on Historical Data. This mandatory feature also needs to be accompanied by an availability of historical data, on which the backtesting can be performed. Integration With Trading Interface. Algorithmic trading software places trades automatically based on the occurrence of a desired criteria. This ensures scalability , as well as integration. A few programming languages need dedicated platforms. While building or buying trading software, preference should be given to trading software that is platform-independent and supports platform-independent languages.
You never know how your trading will evolve few months down the line. The Stuff Under the Hood. It is the trader who should understand what is going under the hood. Thoroughly back-test it before putting it to use with real money.
All ready-made algorithmic trading software usually offers free limited functionality trial versions or limited trial periods with full functionality.
Explore them in full during these trials before buying anything. Do not forget to go through the available documentation in detail. If you plan to build your own system, a good free source to explore algorithmic trading is Quantopian.
It offers an online platform for testing and developing algorithmic trading. The platform also offers built-in algorithmic trading software to be tested against market data. Algorithmic trading software is costly to purchase and difficult to build on your own. Purchasing ready-made software offers quick and timely access, and building your own allows full flexibility to customize it to your needs.
There are two ways to access algorithmic trading software: Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock's high and low prices are temporary, and that a stock's price tends to have an average price over time. An example of a mean-reverting process is the Ornstein-Uhlenbeck stochastic equation. Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc.
When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.
The standard deviation of the most recent prices e. Stock reporting services such as Yahoo! Finance, MS Investor, Morningstar, etc. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.
Scalping is liquidity provision by non-traditional market makers , whereby traders attempt to earn or make the bid-ask spread. This procedure allows for profit for so long as price moves are less than this spread and normally involves establishing and liquidating a position quickly, usually within minutes or less. A market maker is basically a specialized scalper. The volume a market maker trades is many times more than the average individual scalper and would make use of more sophisticated trading systems and technology.
However, registered market makers are bound by exchange rules stipulating their minimum quote obligations. For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented. Most strategies referred to as algorithmic trading as well as algorithmic liquidity-seeking fall into the cost-reduction category. The basic idea is to break down a large order into small orders and place them in the market over time.
The choice of algorithm depends on various factors, with the most important being volatility and liquidity of the stock. For example, for a highly liquid stock, matching a certain percentage of the overall orders of stock called volume inline algorithms is usually a good strategy, but for a highly illiquid stock, algorithms try to match every order that has a favorable price called liquidity-seeking algorithms.
The success of these strategies is usually measured by comparing the average price at which the entire order was executed with the average price achieved through a benchmark execution for the same duration. Usually, the volume-weighted average price is used as the benchmark. At times, the execution price is also compared with the price of the instrument at the time of placing the order. A special class of these algorithms attempts to detect algorithmic or iceberg orders on the other side i.
These algorithms are called sniffing algorithms. A typical example is "Stealth. 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. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.
Strategies designed to generate alpha are considered market timing strategies. These types of strategies are designed using a methodology that includes backtesting, forward testing and live testing. Market timing algorithms will typically use technical indicators such as moving averages but can also include pattern recognition logic implemented using Finite State Machines.
Backtesting the algorithm is typically the first stage and involves simulating the hypothetical trades through an in-sample data period. Optimization is performed in order to determine the most optimal inputs. 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.
Live testing is the final stage of development and requires the developer to compare actual live trades with both the backtested and forward tested models. Metrics compared include percent profitable, profit factor, maximum drawdown and average gain per trade.
As noted above, high-frequency trading HFT is a form of algorithmic trading characterized by high turnover and high order-to-trade ratios. 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.
Among the major U. There are four key categories of HFT strategies: All portfolio-allocation decisions are made by computerized quantitative models. The success of computerized strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.
Market making involves placing a limit order to sell or offer above the current market price or a buy limit order or bid below the current price on a regular and continuous basis to capture the bid-ask spread. 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.
If the market prices are sufficiently different from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit.
HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities. A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes. A 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.
Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage generally consists of buying the stock of a company that is the target of a takeover while shorting the stock of the acquiring company. Usually the market price of the target company is less than the price offered by the acquiring company.
The spread between these two prices depends mainly on the probability and the timing of the takeover being completed as well as the prevailing level of interest rates. The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed.
The risk is that the deal "breaks" and the spread massively widens. One strategy that some traders have employed, which has been proscribed yet likely continues, is called spoofing. It is the act of placing orders to give the impression of wanting to buy or sell shares, without ever having the intention of letting the order execute to temporarily manipulate the market to buy or sell shares at a more favorable price.
This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants. The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed. The trader then executes a market order for the sale of the shares they wished to sell. The trader subsequently cancels their limit order on the purchase he never had the intention of completing.
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.
HFT firms benefit from proprietary, higher-capacity feeds and the most capable, lowest latency infrastructure.
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. Joel Hasbrouck and Gideon Saar measure latency based on three components: 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.
This is due to the evolutionary nature of algorithmic trading strategies — they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets.
Increasingly, the algorithms used by large brokerages and asset managers are written to the FIX Protocol's Algorithmic Trading Definition Language FIXatdl , which allows firms receiving orders to specify exactly how their electronic orders should be expressed. More complex methods such as Markov Chain Monte Carlo have been used to create these models.
Algorithmic trading has been shown to substantially improve market liquidity  among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.
Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity.
Computers running software based on complex algorithms have replaced humans in many functions in the financial industry. While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market.
But it also pointed out that 'greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption'.
UK Treasury minister Lord Myners has warned that companies could become the "playthings" of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company. Other issues include the technical problem of latency or the delay in getting quotes to traders,  security and the possibility of a complete system breakdown leading to a market crash.
They have more people working in their technology area than people on the trading desk The nature of the markets has changed dramatically.
This issue was related to Knight's installation of trading software and resulted in Knight sending numerous erroneous orders in NYSE-listed securities into the market. This software has been removed from the company's systems. Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6, Flash Crash,   when the Dow Jones Industrial Average plunged about points only to recover those losses within minutes. At the time, it was the second largest point swing, 1, And this almost instantaneous information forms a direct feed into other computers which trade on the news.
The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. 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. His firm provides both a low latency news feed and news analytics for traders. 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.
So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said. 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, 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 , 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 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. Released in , the Foresight study acknowledged issues related to periodic illiquidity, new forms of manipulation and potential threats to market stability due to errant algorithms or excessive message traffic.
However, the report was also criticized for adopting "standard pro-HFT arguments" and advisory panel members being linked to the HFT industry. A traditional trading system consists of primarily of two blocks — one that receives the market data while the other that sends the order request to the exchange. However, an algorithmic trading system can be broken down into three parts . Exchange s provide data to the system, which typically consists of the latest order book, traded volumes, and last traded price LTP of scrip.
The server in turn receives the data simultaneously acting as a store for historical database. The data is analyzed at the application side, where trading strategies are fed from the user and can be viewed on the GUI. Once the order is generated, it is sent to the order management system OMS , which in turn transmits it to the exchange.
Gradually, old-school, high latency architecture of algorithmic systems is being replaced by newer, state-of-the-art, high infrastructure, low-latency networks.
The complex event processing engine CEP , which is the heart of decision making in algo-based trading systems, is used for order routing and risk management. With the emergence of the FIX Financial Information Exchange protocol, the connection to different destinations has become easier and the go-to market time has reduced, when it comes to connecting with a new destination.
With the standard protocol in place, integration of third-party vendors for data feeds is not cumbersome anymore.
Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers.
The speeds of computer connections, measured in milliseconds and even microseconds , have become very important. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.
Competition is developing among exchanges for the fastest processing times for completing trades. For example, in June , 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, orders per second. This is of great importance to high-frequency traders, because they have to attempt to pinpoint the consistent and probable performance ranges of given financial instruments.
With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, where a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions. Algorithmic trading has caused a shift in the types of employees working in the financial industry. For example, many physicists have entered the financial industry as quantitative analysts.
Some physicists have even begun to do research in economics as part of doctoral research. This interdisciplinary movement is sometimes called econophysics. Algorithmic trading has encouraged an increased focus on data and had decreased emphasis on sell-side research. Algorithmic trades require communicating considerably more parameters than traditional market and limit orders.
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