Algorithmic Trading .ipynb

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    "## Automated Trading \n",
    "\n",
    "Automated trading refers to the complete automation of the trading process, which involves automation of decision making, order creation, order submission and order execution. It also includes quantitative modelling, risk monitoring and algorithmic trading in most cases.\n",
    "\n",
    "## Algorithmic Trading \n",
    "\n",
    "In short, in trading, the set of instructions or rules is given to the computer (by the trader) to automate the execution of trade orders via the stock exchange with minimal human intervention. This is called algorithmic trading.\n",
    "\n"
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    "# Difference between Automated Trading and Alogorithmic Trading \n",
    "\n",
    "Algorithmic trading just means that the trading strategy follows an algorithm and thus can be backtested with historical data. It can, however, still be executed manually by entering orders by hand. Automated trading takes this a step further, and even executions are completely automated.\n"
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    "# Algorithmic trading strategies\n",
    "Trading strategies are devised by a trader having knowledge of the financial market with regard to\n",
    "\n",
    "- Entering the market (buying when the prices are favourable)\n",
    "- Exiting the market (selling when the prices begin going way below expectations)\n",
    "\n",
    "These strategies are coded as the programmed set of instructions to make way for favourable returns for the trader. The set of instructions to the computer is given in programming languages (such as C, C++, Java, Python). Following which, the computer can generate signals and take the trading position accordingly.These strategies are thus known as algorithmic trading strategies.\n",
    "\n",
    "## Classification of algorithmic trading strategies\n",
    "All the algorithmic trading strategies that are being used today can be classified broadly into the following categories:\n",
    "\n",
    "- Momentum-based strategies or trend following strategy\n",
    "- Arbitrage algorithmic trading strategies\n",
    "- Market making algorithmic trading strategies\n",
    "- Machine learning in trading\n",
    "- Options trading and options trading strategies\n",
    "\n",
    "## Momentum trading strategies\n",
    "algorithmic trading strategy on the market trends which you determined by using statistics. This method of following trends is called momentum trading and the strategies deployed are called as momentum trading strategies.\n",
    " \n",
    "### Strategy paradigms of momentum trading strategies\n",
    "Momentum trading strategies seek to profit from the continuance of the existing trend by taking advantage of market swings.\n",
    "*“In simple words, buy high and sell higher and vice versa.”*\n",
    "\n",
    "**how do we achieve this?**\n",
    "- Short-term positions: In this particular algorithmic trading strategy we will take short-term positions in stocks that are going up or down until they show signs of reversal. It is counter-intuitive to almost all other well-known strategies.\n",
    "\n",
    "- Value Investing: Value investing is generally based on long-term reversion to mean whereas momentum investing is based on the gap in time before mean reversion occurs.\n",
    "\n",
    "- Momentum: Momentum is chasing performance, but systematically by taking advantage of other performance chasers who are making emotional decisions.\n",
    "There are usually two explanations given for any strategy that have  proven to work historically,\n",
    "\n",
    "Either the strategy has compensated for the extra risk that it takes, or\n",
    "There are behavioural factors due to which premium exists.\n",
    "\n",
    "You can create or optimize an intraday momentum strategy using Quadratic Discriminant Analysis.\n",
    "### Why Momentum works!\n",
    "There is a long list of behavioral biases and emotional mistakes that investors exhibit due to which momentum works. However, this is easier said than done as trends don’t last forever and can exhibit swift reversals when they peak and come to an end.\n",
    "\n",
    "Momentum trading carries a higher degree of volatility than most other strategies and tries to capitalize on market volatility.\n",
    "\n",
    "It is important to time the buys and sells correctly to avoid losses by using proper risk management techniques and stop-losses. Momentum investing requires proper monitoring and appropriate diversification to safeguard against such severe crashes.\n",
    "### Modelling ideas of momentum trading strategies\n",
    "First of all, you should know how to detect price momentum or trends.Trends can be detected by following stocks and ETFs that have been continuously going up for days, weeks or even several months in a row.\n",
    "\n",
    "For instance, identify the stocks trading within 10% of their 52-week high or look at the percentage price change over the last 12 or 24 weeks. Similarly to spot a shorter trend, include a shorter-term price change.\n",
    "For instance, back in 2008, the oil and energy sector was continuously ranked as one of the top sectors even while it was collapsing.\n",
    "\n",
    "### Types of momentum trading strategies\n",
    "We can also look at earnings to understand the movements in stock prices. Strategies based on either past returns (price momentum strategies) or earnings surprise (known as earnings momentum strategies) exploit market under-reaction to different pieces of information.\n",
    "\n",
    "- Earnings Momentum Strategies: An earnings momentum strategy may profit from the under-reaction to information related to short-term earnings.\n",
    "- Price Momentum Strategies: A price momentum strategy may profit from the market’s slow response to a broader set of information including longer-term profitability.\n",
    "\n",
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    "# Arbitrage trading strategies\n",
    "If you are planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after), then you are using an event-driven strategy.\n",
    "\n",
    "Bankruptcy, acquisition, merger, spin-offs etc. could be the event that drives such kind of an investment strategy. These arbitrage algorithmic trading strategies can be market neutral and used by hedge funds and proprietary traders widely.\n",
    "\n",
    "## Statistical arbitrage trading strategies\n",
    "When an arbitrage opportunity arises because of misquoting in prices, it can be very advantageous to the algorithmic trading strategy. Although, such opportunities exist for a very short duration as the prices in the market get adjusted quickly. And that’s why this is the best use of algorithmic trading strategies, as an automated machine can track such changes instantly.\n",
    "\n",
    "For instance, assume that each time that Apple‘s stock prices fall by $1, Microsoft’s prices too fall by $0.5. Now, given the case that Microsoft has not fallen yet, you can go ahead and sell Microsoft to make a profit.\n",
    "\n",
    "## Strategy paradigms of statistical arbitrage trading strategies\n",
    "If market making is the strategy that makes use of the bid-ask spread, statistical arbitrage seeks to profit from the statistical mispricing of one or more assets based on the expected value of these assets.\n",
    "\n",
    "A more academic way to explain statistical arbitrage is to distribute the risk between a thousand to a few million trades in a very short holding span with the expectation of gaining profit from the law of large numbers. Statistical arbitrage Algorithms are based on the mean reversion hypothesis, mostly as a pair. \n",
    "\n",
    "## Modelling ideas of statistical arbitrage trading strategies\n",
    "Pairs trading is one of the several strategies collectively referred to as Statistical Arbitrage Strategies. In a pairs trade strategy, stocks that exhibit historical co-movement in prices are paired using fundamental or market-based similarities.\n",
    "\n",
    "The strategy builds upon the notion that the relative prices in a market are in equilibrium, and that deviations from this equilibrium eventually will be corrected.\n",
    "\n",
    "When one stock outperforms the other, the outperformer is sold short and the other stock is bought long, with the expectation that the short-term diversion will end in convergence. This often hedges market risk from adverse market movements i.e. makes the strategy beta neutral.\n",
    "\n",
    "However, the total market risk of a position depends on the amount of capital invested in each stock and the sensitivity of stocks to such risk.\n",
    "\n",
    "In a mature market, arbitrage opportunities are easily identified, and it appears that there may be untapped possibilities for individuals. This EPAT trading project, specifically examines the futures market in China using Kalman Filter Techniques and Statistical Arbitrage.\n",
    "\n",
    "\n",
    "\n",
    "\n"
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    "# Market making trading strategies\n",
    "Definition of a market maker:\n",
    "* “A market maker or liquidity provider is a company, or an individual, that quotes both a buy and sell price in a financial instrument or commodity held in inventory, hoping to make a profit on the bid-offer spread, or turn.” *\n",
    "\n",
    "Market making provides liquidity to securities which are not frequently traded on the stock exchange. The market maker can enhance the demand-supply equation of securities.\n",
    "example:\n",
    "Assume you have Martin, a market maker, who buys an asset for INR 500 from the market and sell it at INR 505. He will give you a bid-ask quote of INR 505-500. The profit is of INR 5 but during the day, he indulges in a lot of shares to pocket a significant chunk and offset the risk of price movement against him. Also, when Martin takes a higher risk, the profit probability is also higher.\n",
    "\n",
    "Michael Lewis’ book ‘Flash Boys’ is good book havbing great details on liquidity, market making and HFT(high frequency trading).\n",
    "## Strategy paradigms of market making trading strategies\n",
    "**Some trading algorithms tend to profit from the bid-ask spread.**\n",
    "For instance, Martin being a market maker is a liquidity provider who can quote on both the buy as well as the sell side in a financial instrument hoping to profit from the bid-offer spread.\n",
    "\n",
    "Martin will accept the risk of holding the securities for which he has quoted the price and once the order is received, he will often immediately sell from his own inventory. He might seek an offsetting offer in seconds and vice versa.\n",
    "\n",
    "**When it comes to illiquid securities, the spreads are usually higher and so are the profits.**\n",
    "For instance, Martin will take a higher risk in this case. Several segments in the market lack investor interest due to a lack of liquidity as they are unable to gain exit from several small-cap stocks and mid-cap stocks at any given point in time.\n",
    "\n",
    "Market makers like Martin are helpful as they are always ready to buy and sell at the price quoted by them. In fact, much of high-frequency trading (HFT) is passive market making. The strategies are present on both sides of the market (often simultaneously) competing with each other to provide liquidity to those who need it.\n",
    "\n",
    "## Modelling ideas of market making strategies\n",
    "The bid-ask spread and trade volume can be modelled together to get the liquidity cost curve which is the fee paid by the liquidity taker.\n",
    "\n",
    "If the liquidity taker only executes orders at the best bid and ask, the fee will be equal to the bid-ask spread times the volume. When the traders go beyond the best bid and ask taking more volume, the fee becomes a function of the volume as well.\n",
    "\n",
    "Trade volume is difficult to model as it depends on the liquidity taker’s execution strategy. The objective should be to find a model for trade volumes that is consistent with price dynamics.\n",
    "\n",
    "Market-making models are usually based on one of the two:\n",
    "\n",
    "- First model of market making: The first focuses on inventory risk. The model is based on preferred inventory position and prices based on the risk appetite.\n",
    "- Second model of market making: The second is based on adverse selection which distinguishes between informed and noise trades. Noise trades do not possess any view on the market whereas informed trades do. When the view of the liquidity taker is short-term, it aims to make a short-term profit utilizing the statistical edge.\n",
    "\n",
    "In the case of a long-term view, the objective is to minimize the transaction cost. The long-term strategies and liquidity constraints can be modelled as the noise around the short-term execution strategies.\n",
    "\n",
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    "# Machine learning trading strategies\n",
    "In machine learning based trading, one of the applications is to predict the range for very short-term price movements at a certain confidence interval. The advantage of using Artificial Intelligence (AI) is that humans develop the initial software and the AI itself develops the model and improves it over time. \n",
    "A large number of funds rely on computer models built by data scientists and quants but they’re usually static, i.e. they don’t change with the market. Machine Learning algorithmic trading models, on the other hand, can analyze large amounts of data at high speed and improve themselves through such analysis.\n",
    "\n",
    "## Modelling idea for Machine Learning trading strategies\n",
    "Machine learning model selection for algorithmic trading involves a careful evaluation of various models to identify the most suitable and effective model which can be sued for making informed trading decisions.\n",
    "\n",
    "A form of machine learning called “Bayesian networks” can be used to predict market trends while utilizing a couple of machines.\n",
    "\n",
    "An AI which includes techniques such as 'Evolutionary computation' (which is inspired by genetics) and deep learning might run across hundreds or even thousands of machines.\n",
    "\n",
    " \n"
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    "# AI scope in Automated Trading \n",
    "- It can create a large and random collection of digital stock traders and test their performance on historical data.\n",
    "- It then picks the best performers and uses their style/patterns to create a new of evolved traders.\n",
    "- This process repeats multiple times and a digital trader that can fully operate on its own is created.\n"
   ]
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    "# Options trading strategies\n",
    "Options trading is a type of trading strategy. It is a perfect fit for the style of trading expecting quick results with limited investments for higher returns. One can create their own options trading strategies, backtest them, and practise them in the markets.\n",
    "## Types of Options trading strategies\n",
    "- Diagonal Spreads\n",
    "- Calendar Spread\n",
    "- Synthetic Long Put\n",
    "- Long Combo\n",
    "- Bear Spread\n",
    "- Bear Call Ladder\n",
    "- Collar Options\n",
    "- Bull Call Spread\n",
    "- Butterfly Spread\n",
    "- Straddle Options\n",
    "- Jade Lizard\n",
    "- Iron Butterfly\n",
    "- Long Strangle\n",
    "- Iron Condor\n",
    "- Broken Wing Butterfly\n"
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    "# Risk management in algorithmic trading strategies\n",
    "Integrating risk management into algorithmic trading systems is essential for safeguarding against potential losses and ensuring the overall stability and success of algo trading strategies. Algorithmic trading strategies rely on complex mathematical models and automation. The success of an algorithmic trading strategy requires effective risk management to navigate market conditions which might be unpredictable at times.\n",
    "\n",
    "Thus, a comprehensive risk management framework is crucial for sustainability and success of an algorithmic trading strategy or algorithmic trading in general. It ensures adaptability to market dynamics and informed decision-making.\n",
    "\n",
    "The following techniques might be useful:\n",
    "\n",
    "**Risk Mitigation Techniques:**\n",
    "\n",
    "- Use of stop-loss orders to limit losses on individual trades.\n",
    "- Diversification of portfolios to spread risk across different assets.\n",
    "\n",
    "**Dynamic Adjustments:**\n",
    "\n",
    "- Continuous monitoring and adjustment of algorithm parameters.\n",
    "- Adapting to evolving market conditions for effective risk management.\n",
    "- Quick responses to unforeseen events to reduce the likelihood of significant losses.\n",
    "\n",
    "**Backtesting and Simulation:**\n",
    "\n",
    "- Testing your algorithms using historical market data.\n",
    "- Assessing performance of your algo trading strategy under various scenarios to identify weaknesses.\n",
    "- Refining algorithms based on insights gained from testing to make your algo trading strategy robust\n",
    "\n"
   ]
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    "# Where are algo trading strategies used?\n",
    "Algo trading strategies can be used by any trader having experience in the financial markets armed with coding skills. Such a trader would be termed an algorithmic trader. An algorithmic trader can code the algorithmic trading strategy to take different actions regarding trade orders.\n",
    "\n",
    "An algo trading strategy can perform complex mathematical calculations for predicting prices of financial assets.\n",
    "An algo trading strategy can Forecast market movements\n",
    "One can generate trading signals with an algo trading strategy\n",
    "Risk Management is possible with algo trading strategies\n",
    "Algorithmic trading strategies are used by:\n",
    "\n",
    "hedge funds,\n",
    "investment banks,\n",
    "pension funds,\n",
    "proprietary traders and\n",
    "broker-dealers for market making."
   ]
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