Modern Portfolio Theory (MPT) is a method to select which stocks and what amounts to buy such that as a group, these stocks give the highest amount of returns for a given amount of risk.

In this article, risk is defined as the volatility[2] of your returns[1].

[1] Returns are the money made or lost over a period of time.

[2] Volatility refers to returns that change rapidly and unexpectedly.

Understanding Modern Portfolio Theory

The 2 main ideas behind MPT are:

1. Preference for higher reward per risk
2. Risk cancellation

Preference for higher reward per risk

Let’s say you are betting at a casino and you have 2 games to choose from.

• In the first game, you gain $5 if you win. But if you lose, you’re out $3.
• In the second, you could win $5, but if you lose, you are down $1.

Assume that the odds of winning is the same for both games.

Which will you choose?

I hope you choose the second game. If you chose the first, please go ahead and close your trading account now.

This shows that for a given set of returns, we naturally want to risk as little as possible.

Similarly, if we were to compare 2 stocks with the same gain, we prefer the one with less volatility.

We prefer Stock X!

Risk cancellation

Let’s talk about stocks now.

To understand risk cancellation, we look at 2 stocks.

Stock A looks like this:

Stock B looks like this:

Both Stock A and B look similar. They both move up.

However, there is a difference, they are inversely correlated in the short run.

Combining Stock A and B

Both Stock A and B moves up at the same rate (i.e. have the same returns).

However, their short term moves cancel out.

As an example, if we own $50 of Stock A and $50 of Stock B, our $100 total investment can be represented by the red line.

The red line is better than owning $100 of either Stock A or Stock B alone as it is less volatile.

Remember we want the highest returns per risk (where risk is defined as the volatility of return).

So, what is MPT really?

MPT tells us to choose stocks in a way that their risks cancel out and that the overall returns are the highest for the amount of risk we take on.

Increasing stocks to cancel out more risks

One more concept of MPT is the required number of stocks for risk cancellation.

Using 2 stocks to cancel risks might not be ideal as it is difficult for the 2 stocks to be inversely correlated (in the short term) all the time.

If we have 3 stocks, it would be easier. If we have 4, it’s even easier.

However, each stock addition reduces risks to a lesser extent. The reason is, as our overall risk declines, there isn’t much risk left to eliminate.

Legendary hedge fund manager Ray Dalio describes this better than me: Ray Dalio breaks down his “Holy Grail”

A key takeaway from the video: Once you have about 10 assets in your portfolio, every additional asset provides minimal risk reduction.

One thing to note – when the risks to any individual stock are minimized, the main risk that is left is the exposure to the overall market movement.

Note that we use stocks in our examples but MPT applies to any asset – Commodities, Fixed Income, Forex, Cryptocurrency, etc.

Why is MPT important to you?

3 reasons.

Being able to size up to make more profits!

If your portfolio has low volatility, you will be able to increase your bet size without fear of losing it all.

Here is what happens when you double the amount you trade when your portfolio is volatile.

You hit $0 and get wiped out.

Here is what happens when you double the amount you trade when your portfolio is less volatile.

You stay in the game and make more monies!

Staying in the game

Even if your account does not hit zero. You might be in an unrecoverable position.

If you lose 50% need to make 100% to recover.

If your portfolio is worth $100 and you lose $90, you’ll only have $10 left. You need to make 1000% to hit $100 again.

Thus, if your returns are too volatile, you might not be able to recover.

Examples of Modern Portfolio Theory

Modern Portfolio theory has inspired certain specific portfolios such as the Ray Dalio All Weather Portfolio.

The post What is Modern Portfolio Theory? appeared first on AlgoTrading101 Wiki.

Backtesting is the process of testing a trading or investment strategy using data from the past to see how it would have performed.

Understanding backtesting

Running a backtest

The general idea of a backtest is to run through stock prices in the past, usually with software, and hypothetically firing trades based on a certain trading strategy.

For example, let’s say your trading strategy is to buy Amazon when it falls 3% in a day, your backtest software will check Amazon’s prices in the past and fire a trade when it fell 3% in a day.

The backtest results will show if the trades were profitable.

Backtesting can be as simple as running analysis in Excel to something more complex such as creating custom backtesting software. It all comes down to your individual requirements.

The Aim of Backtesting

Backtesting accomplishes 3 things:

1. Shows if a strategy performs well in periods when it is supposed to, and vice versa
2. Provides an understanding of how the strategy performs in different markets.
3. Produces insights on how the strategy might be improved on.

1. Performance during selected periods

With a backtest, we can check to see if a strategy makes money when it is supposed to and loses money when it is supposed to.

For instance, let’s say that our strategy is expected to perform better when the markets are volatile, or in other words, when they move much more than they normally do.

If our backtests then show that we make more money than expected during less volatile periods, this is a red flag (even though we made money).

We need to examine our strategy and figure out why.

2. Understand how the strategy performs in different markets

To gain more confidence over how consistently a trading strategy will perform, backtests can be run in different market environments.

This means running backtests with different stocks or other market assets.

It could also mean performing tests during periods where there are clear trends and comparing them to periods where there weren’t.

3. Improving the strategy

This involves making changes to a strategy after looking through the results of the backtest.

A common pitfall here is to continuously tweak the strategy so that it shows better results in a backtest.

This approach rarely leads to profitability when you trade it with real money and is known as overfitting.

Why is backtesting important to you?

Backtesting is an essential part of developing a trading strategy.

Pass, improve, or fail

A backtest can help decide if a strategy is suitable to trade real money, can use improvement, or if it’s best to give up on it.

Deploying a strategy

As mentioned, backtesting helps us understand how our strategy performs in different market environments, this will allow us to deploy our strategy better.

A trader might have multiple strategies. By knowing the strength and weaknesses of each of the strategies, it will be clear when is it best to deploy a certain strategy.

Certain strategies pair well with others. Some strategies don’t work well when market conditions are unfavorable. Running backtests will provide the information needed to decide when to deploy which.

Examples of Backtests

There are several tools available to help you conduct a backtest. 

A common way is to use a trading platform. Popular trading platforms that include backtesting capabilities include MetaTrader4, Tradingview, ThinkorSwim, and Ninjatrader.

The benefit of using such a platform is that most of them include the necessary data. Several of them also have built-in analysis.

As an alternative to using a solution tied to a trading platform, there are several coding libraries that can help in backtesting.

For those familiar with Python, Backtrader and Zipline are both great options.

These libraries can accommodate a lot of customization. The trade-off is that the learning curve is a bit steeper.

Analysis tools usually come with backtesting software. There are also third-party solutions available such as Pyfolio. This Python library simplifies creating charts and calculating statistics.

Backtesting software is not a necessity. The above image shows a chart that tests a hypothesis that gold prices rally for a few days after a Fed meeting. 

All it took was a simple charting library and some historical data.

The post What is Backtesting? appeared first on AlgoTrading101 Wiki.

Understanding Hedge Funds

Hedge funds usually take money from high net-worth individuals or other companies. These people, or companies, are clients of the hedge fund.

Note that the money the client hands over to the hedge fund belongs to the client. The hedge fund controls how the money is invested but does not take ownership of the funds.

How do hedge funds make money?

They make money by taking a cut of the profits from the investment of their clients’ money.

What if they lose money?

If the hedge fund loses money, the clients bear all the losses.

How much of a cut do hedge funds make? (Hedge Fund Fee Structure)

In the past, it was common to charge a 2% management fee along with a 20% performance fee.

Management fee:

A management fee is a fixed yearly fee and hedge funds will collect it whether the fund makes a loss or a profit.

The management fee, which is usually 2%, is deducted from the total amount invested by the client.

As an example, if a client invests $100 million with a hedge fund, they will need to pay $2 million as a management fee. It doesn’t matter if the fund makes or loses money that year, this fee has to be paid.

Hedge funds will typically deduct the management fee every month, pro-rated from the total yearly fee.

Performance fee:

A performance fee is collected from the total amount of profits a fund makes. If the fund loses money, the performance fee is $0 (it does not return money to investors).

The usual 20% performance fee is deducted from any profits the fund makes.

For instance, if the client places $100 million with a hedge fund and the hedge fund makes $20 million from that $100 million, the hedge fund’s cut is 20% of that $20 million or $4 million.

Clients usually pay performance fees on a quarterly basis.

Trend of Decreasing Fees

Hedge fund fees have consistently declined over the years as poor returns have led to pressure by clients for lower fees.

Active vs Passive Fund Management

Hedge funds fall under the category of active management. This means that the fund manager and his/her team make day-to-day decisions regarding investments.

Mutual funds are a good example of passive management. The mutual fund manager makes broad investment decisions and reviews them sporadically.

Alpha vs Benchmark-Tracking

Alpha funds are a type of fund that aims to make profits no matter how the market is performing.

Benchmark-tracking funds are a type of fund that attempts to perform better than an index. An index offers a value for comparison, derived from the performance of a group of stocks or other assets.

They choose a specific index as a reference target and aim to outperform it.

A common target index is the S&P 500 and several benchmark-tracking fund have set their aim to outperform it. If the S&P 500 gains, they must gain more. If the S&P 500 loses, they are content if they lose less.

Quantitative vs Non-Quantitative Fund

Quantitative funds use mathematical or high-tech methods to generate profits. Such methods include:

• analyzing and exploiting big data
• alternative data (credit card information, location data, etc.)
• computing speed
• machine learning

Non-quantitative funds use qualitative methods to generate alpha. Such methods include reading annual reports, talking to a company’s management, evaluating a company’s CEO and understanding the macroeconomic conditions of a country.

Examples of Hedge Funds

Here are some famous hedge funds.

• Bridgewater Associates (Qualitative)
• Renaissance Technologies (Quantitative)
• AQR Capital Management (Mixed)

How are Hedge Funds Important to You?

If you are rich and are choosing which hedge fund to put your money in, then knowing how hedge funds work is useful.

Otherwise, hedge funds are not important to you.

The post What is a Hedge Fund? appeared first on AlgoTrading101 Wiki.

Turtle trading refers to the way a group of traders in the 1980s traded. This group, who is known as the turtle traders, made $175 million in 5 years using a fixed strategy that was taught to them.

Description

History of the Turtle Traders

In the 1980s, Richard Dennis and William Eckhardt developed a systematic trading system that turned $5,000 into $100 million (a lot of money in the 1980s).

Dennis believed successful traders can be trained while Eckhardt believed they are born with a gift for trading. To settle this debate, they started an experiment that will go on to be world famous.

Turtle Experiment

Dennis picked people off the streets, interviewed them and selected a handful for the experiment. He and Eckhardt taught these traders (they were called the Turtles) how to trade for two weeks.

They gave the Turtles money to manage after the training.

Why the name “Turtles”? Dennis, visited a turtle farm in Singapore and believed he could “grow” traders and efficiently as they grow turtles there.

The result of the experiment

The Turtles made $175 million in 5 years.

The Turtle Trading Strategy

The Turtle Traders used a long term breakout strategy. They mainly traded Forex and commodity futures.

Their strategy is based on fixed rules and the traders are required to abide by it strictly.

Entries and Exits

Whenever an asset rise significantly, they will long (i.e. buy) the asset. They will long more as the asset continues to rise.

Whenever an asset falls significantly, they will short (i.e. bet that it falls) the asset. They will short more as the asset continues to fall.

In both long and short trades, they will close the trade when it moves significantly against them. This means that they give back some profits near the end of the trade. However, it also means that they will stick will a trend for a long time.

As it takes discipline not to close a trade when taking on a significant loss. Many Turtle Traders fail to make the cut due to the lack of discipline in this aspect.

Position Sizing and Risk Management

The Turtle Traders strength is their position sizing (how much to bet) and risk management

They use the volatility of the markets to determine how much to trade. The more volatile the markets are, the less they bet per trade.

Their trades are spread over many different assets so as to diversify their risks. The traders’ overall positions can’t be overwhelming long or short.

The Original Turtle Trading Strategy

Link: The Original Turtle Trading Strategy – Tradingblox.com

The Turtle Trading Strategy Today

There is little evidence that the original trading strategy works today.

However, many former Turtle Traders continued to be successful traders, using techniques that are similar but not identical, to the original Turtle Trading Strategy

Links to Other Explanations

• Turtle Trading – Investopedia
• Turtle Trading Rules – Business Insider

Related Terms

• Algorithmic Trading Strategies
• Backtesting Biases and Risks
• Market Inefficiencies

The post Turtle Trading appeared first on AlgoTrading101 Wiki.

An option gives the option holder a choice to buy or sell a pre-agreed asset at a certain pre-agreed price.

Description

There are 2 main types of options: 1) Call option and 2) Put option.

Call options gives the option holder a choice to buy a pre-agreed asset at a certain pre-agreed price.

Put options gives the option holder a choice to sell a pre-agreed asset at a certain pre-agreed price.

This pre-agreed asset is called the underlying asset, in other words, it is the asset that is attached to this option.

This pre-agreed price is called the strike price.

Example of an Option Trade

Call option example:

Trader A buys the Apple’s call option at strike price $190. In this case, Apple is the underlying asset.

Let’s assume Apple is trading at $180 today. Trader A’s call option is not very valuable as we can buy Apple stock directly for $180. Thus, we don’t want to exercise the choice to buy it at $190 (a more expensive price).

If Apple goes to $200 tomorrow, Trader A’s call option becomes more valuable. We can now exercise our choice to buy Apple at $190 and immediately sell Apple at the exchange for $200 (a $10 profit).

Put option example:

Trader B buys the Google’s put option at strike price $1200.

Let’s assume Google is trading at $1300 today. Trader B’s put option is not very valuable as we can sell Google in the market for $1300. Thus, we don’t want to exercise the choice to sell it at $1200 (a lower price).

If Google drops to $1100 tomorrow, Trader B’s put option becomes more valuable. We can now immediately buy Google from the exchange at $1100 and exercise our choice to sell Google at $1200 (a $100 profit).

Payoff Charts

We use charts to get a better look at our payoffs, i.e. how much we will make (or lose) as the underlying asset’s price moves.

From the diagram above, we can see that when the payoff is:

• $50. The payoff is $0.
• $40. The payoff is $10.
• $30. The payoff is $20.
• $20. The payoff is $30.

This makes sense as if we own this put option when the underlying is $30, we can immediately buy the underlying from the exchange at $30, and exercise our choice to sell it at $50. Thus, netting a $20 profit.

To exercise an option means to buy or sell the underlying assets at the pre-agreed strike price.

Moneyness

Moneyness describes how we name options at different profit or loss levels. There are 3 terms used to describe this:

1. In-the-money (ITM): Our payoff is positive.
2. At-the-money (ATM): Our payoff is zero. Strike price = Underlying asset’s price.
3. Out-of-the-money (OTM): Our payoff is zero (or negative after considering the cost of the option).
   • For call options, strike price is greater than underlying asset’s price.
   • For put options, strike price is less than underlying asset’s price.

Expiration

Options expire at a certain pre-agreed date.

If the options holder did not exercise the option by then, the option becomes worthless if it is ATM or OTM. If the option is ITM, it will be automatically exercised.

Cost of Options (Premiums)

If the stock goes higher than your call option strike price, you make profits. If it doesn’t, you don’t make losses. It might seem like a deal too good to be true.

And yes, it is.

The catch is that you need to pay to buy call or put options. The costs of the option is known as the premium.

The premium goes up as the the option gets more ITM as we need to take into account the profit already accumulated by this option.

The premium goes down as the option gets less ITM as we need to take into account that the profit is far in sight.

The premium shifts your diagram down, as you need to earn the premium amount before you go on to make a profit.

This is an example of some of Apple’s options as viewed in a trading terminal:

Selling Options

Traders are able to sell options too. The payoff will be the exact opposite of that of an option buyer.

In this case we are selling that option without first owning it. When you do that, you are said to have shorted that option.

Option buyers pay a premium and hope that the option goes ITM.

Option sellers receive a premium and hope that the option does not go ITM.

In this aspect, options seller have a limit on how much they can make but their potential losses are unlimited. Vice versa for option buyers.

Option Combinations – Strategies

You can combine different options to create unique structures. Here is a short non-exhaustive list:

• Straddle
• Put Spreads
• Iron Condor

Straddle

When Trader A buys a call option and a put option at the same strike price, the resulting position is a long straddle.

The above is your payoff diagram when you long a straddle.

You long a straddle when you want to bet that there is going to be a big move, but you’re not sure if the move will be up or down.

The cost of this trade is double that of a regular call or put option.

The above is your payoff diagram when you short a straddle.

This involves selling a call and put option at the same strike price.

You short a straddle when you want to bet that there isn’t going to be much movement.

Put Spread

When Trader A sells a put option at strike price A and buys a put at strike price B (where B is greater than A), the resulting position is a long put spread.

The rationale of this trade is that you want to bet that the underlying asset will fall by a little. But you want some insurance to protect yourself if the price rises.

The long put option at strike price B will provide that insurance. The cost of this insurance is the premium of that put option.

A long put spread is also known as a bear put spread or vertical spread.

When Trader A buys a put option at strike price A and sells a put at strike price B (where B is greater than A), the resulting position is a short put spread.

The rationale of this trade is that you want to bet that the underlying asset will rise by a little. But you want some insurance to protect yourself if the price falls.

The long put option at strike price A will provide that insurance. The cost of this insurance is the premium of that put option.

A short put spread is also known as a bull put spread or vertical spread.

Iron Condor

The iron condor is formed by buying a put option at strike A, selling a put option at strike B, selling a call option at strike C and buying a call option at strike D.

The rationale of this trade is that you want to bet that the underlying stock price will stay between B and C.

You give up some profits (when the underlying is between B and C) in order to gain some premiums from selling 2 options, to reduce the cost of this structure.

We have only listed 3 option strategies that can be made by combining options and stocks. Here are 40 more option strategies: Options Strategies – Optionsplaybook.com

Why Trade Options

• To create creative trades by trading structures as seen in the above section.
• To hedge certain exposure. Think of this as buying insurance on your trades by giving up potential gains.
• To leverage. Option premiums are relatively low compared to the potential gains. (This doesn’t mean trading options is generally profitable.)

Regular Options

These are common options that are traded in the markets.

American Options

These are the most common options which can be exercised anytime. Despite it’s name, it has nothing to do with American stocks.

European Options

These are options which can only be exercised on expiration. Despite it’s name, it has nothing to do with European stocks.

Exotic Options

There also exist unique options with quirky features.

Barrier Options

These come into existence or become worthless once the underlying asset reaches a certain pre-agreed price.

Asian Options

Asian options’ payoffs are determined by the average price of the underlying asset over a pre-set period of time.

Basket Options

Basket Options are based on more than one underlying asset. The payoff of the basket option is based on the average price of a group of underlying assets.

Lookback Options

Lookback options have no strike price initially. On the expiry date, the option holder will choose a strike price among all the prices that have occurred during the lifetime of the option. Usually the holder will choose the most favorable strike price.

We have covered 4 exotic options, read about another 8 more here: What are Exotic Options – Corporate Finance Institute.

Links to Other Explanations

• Options – Investopedia
• Option (Finance) – Wikipedia

Related Terms

• Algorithmic Trading Strategies
• Stochastic Calculus
• Backtesting Biases and Risks

The post Options Trading Basics appeared first on AlgoTrading101 Wiki.

Backtesting biases refer to how the results of a trading strategy backtest can be misleading.

Description

Here are the 8 common biases:

1. Black Swan Reconciliation
2. Survivorship Bias
3. Spreads
4. Cost of carry/Holding costs
5. Inaccurate Price Simulation
6. Change in Contract Specifications
7. Look-ahead Bias
8. Curve-Fitting and Optimization Bias

Bias 1 – Black Swan Reconciliation

Black swan events refer to events that come as a surprise and have a huge impact.

Brokers and exchanges may alter the prices of assets after a volatile price moves (black swan events). There are 2 types of alteration.

Type 1 – Changing the fill price

After an unexpected large price move, brokers and exchanges might change the prices that you got filled on your trades.

Example

EURUSD is trading at 1.1300. A black swan event occurs and EURUSD spikes up 2000 pips (to 1.3300) (1 pip = $0.0001).

You long EURUSD 1000 pips into the 2000 pips move. You are long EURUSD at 1.2300. It is now trading at 1.3300. You close the trade at a 1000 pips profit.

A few hours after the trade, you receive an email saying that “In view of this unexpected event, all trades will be cleared at 1.1800 price”.

Your 2000 pips profit becomes a 500 pips loss. Your account gets wiped out.

Real example: Saxo Trades Lawsuits With Clients After Swiss Currency Turmoil

Type 2 – Changing their historical price

After an unexpected large price move, brokers and exchanges might not change the prices that you got filled on your trades.

However, they alter the price on the historical charts and data. Thus, the prices you see in your charts are different (almost always worse) than the prices you get in live trading.

In your backtests, you might have bought Apple shares at $180, but in real life, you would have gotten those shares at $250.

Bias 2 – Survivorship Bias

Survivorship bias, or survival bias, refers to the fact that people overlook entities/processes that failed because they only see successful entities/processes.

Example

We are selecting a bunch of stocks to trade. We create a list of criteria to identify potentially successful stocks.

Next, we filter the universe of stocks listed in the US based on these criteria.

And with that, survivorship bias just got to us. This universe of stocks only includes stocks that survive. There may be stocks that are delisted but fit our criteria.

We need to consider those stocks as well to give us an idea of how sound our strategy is.

Bias 3 – Spreads

The difference between the price we can buy at (bid price) and the price we can sell at (ask price) is called the spread.

Spreads change in real time. It depends on the buyers and sellers on exchanges, or brokers.

During volatile events, spreads usually widen, sometimes by a 100 times.

Without accurate bid and ask data, these spread widening events will make our backtests inaccurate.

Bias 4 – Cost of carry/Holding costs

If you are leveraged (you trade a size larger your capital by borrowing from the broker), shorting or trading a derivative, you might need to pay interest to hold your positions.

This interest represents the fees needed to cover the capital loaned to you, or the costs to hold any underlying assets.

These holding costs might vary without warning during the lifetime of a trade. Hence, it is difficult to estimate these costs in your backtest.

Example

The usual interest cost to short a stock is less than 2% a year.

However, for a period in early 2019, the cost to short Tilray, a cannabis stock shot up to over 800% a year.

Bias 5 – Inaccurate Price Simulation

Not all backtesters replicate the exact historical price movement, some use simulated fake price movements.

This might not be significant if you make a few trades a year and analyze the market using end-of-day data.

However, your backtest results will be greatly skewed if your strategy is related to scalping (price action and movement) and fires many trade per day on lower timeframe data.

Bias 6 – Change in Contract Specifications

An exchange or broker may change the contract specifications (i.e. details) of their products.

For instance, they may increase margin requirements, change the settlement specifications or contract size of their products. These may lead to jumps in market prices.

The main takeaway here is – in such cases, do not take a price change at face value. Your P&L may not change proportionally to a price change.

For instance, increasing the margin requirement for silver may cause silver prices to fall. In your backtest, your short silver position may look like it is doing well. However, if you had traded that move in real-life, you may get a margin call and be forced to close the position.

Real-life examples

• Margin change didn’t cause silver slide – CME
• ICE Changes Gold Contracts Specifications, Opts for Physical Delivery of Bullion

Bias 7 – Look-ahead Bias

Look-ahead bias involves having prior knowledge of how the market behaves before running a backtest.

Example

You want to run a strategy that takes advantage of trends. You look for assets that trend and discard those that don’t trend.

You then run a backtest on these assets using a trending strategy. Unsurprisingly, your strategy does well.

These tests are not useful as you have only chosen assets that you know would have done well in your backtests.

Bias 8 – Curve-Fitting and Optimization Bias

Curve fitting is the process of adapting a trading system so closely to the past that it becomes ineffective in the future.

Optimizing strategies too closely to past data will result in inflexibility to adapt to the future. Hence, it leads to poor performance in the future.

We need to adapt our trading strategies to signals in historical data, not noise.

Links to Other Explanations

• Backtesting Biases and How To avoid them – Auquan
• Backtesting bias and how we avoid it – Keepstock
• 9 Mistakes Quants Make that Cause Backtest to Lie – Dr Balch

Related Terms

• Algorithmic Trading Strategies
• Backtesting (Upcoming)
• Market Inefficiencies

The post Backtesting Biases and Risks appeared first on AlgoTrading101 Wiki.

High-frequency trading (HFT) describes trading that require high computing and communication speeds.

Description

HFT is characterized by high communication and computing speed, large number of trades, low profit per trade and expensive software infrastructure.

High-frequency traders use communication speed to profit and outwit other traders.

Strategy types

Arbitrage

Main article: Arbitrage

Arbitrage trades happen when an asset is priced differently on 2 exchanges and a trader buys the cheaper one while shorting the pricier one.

Reaction to news

When a major news is released, the trader who reacts the fastest wins. In this case, the high-frequency trader needs to analyze the news and fire the trade before everyone else

Latency Arbitrage

When a traditional (slower) hedge fund buys a large amount of Stock A, a HFT hedge fund will detect that.

The HFT hedge fund will then buy all the Stock A on the other exchanges and sell it back to the slower hedge fund for a small profit.

The HFT hedge fund might do this millions of times over a day.

Statistical Arbitrage

A large number of similar stocks might move in a similar manner. When any of the stocks diverge, the high-frequency trader will buy the cheaper one and/or short the pricier one.

Index Arbitrage

An index or exchange-traded fund is designed to track the returns of an index such as the S&P500.

Other strategies

HFT is a secretive field. Once a strategy is revealed and the other funds join in, the profit opportunity disappears fast.

Thus, many new strategies are innovated everyday and are not known to the general public.

Investment in infrastructure

HFT funds spend hundreds of millions on hardware and software infrastructure to reduce their computing and communication speed by the milliseconds.

Since relative speed is more important than absolute speed, HFT funds constantly try to be faster than their rivals.

Investments in infrastructure includes building a straight tunnel to lay communication lines and shifting their servers right beside the exchange’s servers.

Examples of Popular High-Frequency Firms

• Virtu Financial 
• Citadel Securities 
• Two Sigma Securities 
• Tower Research Capital

Links to Complicated Explanations

• High-Frequency Trading – Investopedia
• High-Frequency Trading – Wikipedia

Related Terms

• Statistical Arbitrage (Coming soon)
• Exchange-traded fund (Coming soon)
• Machine Learning

The post High-Frequency Trading appeared first on AlgoTrading101 Wiki.

Cryptocurrencies are digital money.

Description

Cryptocurrencies are digital money that are tracked using a virtual accounting system called the blockchain.

Cryptocurrencies are stored in virtual wallets. The blockchain records the number of cryptocurrency coins every wallet has and all the transfers that have ever occurred.

Anyone can verify the transactions in the blockchain by setting up computers or servers to ensure all the transactions tally.

In exchange, they are rewarded with coins for their work. Those who do this are called miners.

The most common cryptocurrency coins are Bitcoin (BTC) and Ethereum (ETH).

3 Broad Uses of Cryptocurrencies

There are many cryptocurrencies with different uses. They fall into 3 broad categories.

Store of value, medium of exchange

Cryptocurrencies can be spent to buy goods or services. In this aspect, they are similar to fiat currencies or gold.

Smart contracts

A smart contract is a code that enforces certain action when an event occurred.

Example: If Wallet A receives Coin X, Wallet A will return Coin Y to the sender of Coin X.

In this case, the smart contract is attached to Wallet A. The action and events coded onto the smart contract has to be digitally trackable.

Smart contracts can interact with non-digital events if an entity is appointed to input the results of the non-digital event. This entity is known as an oracle.

Example: If the temperature in City X falls below 32 degrees Fahrenheit, based on weather.com, Wallet A must send 10 coins to Wallet B. In this case, weather.com is the oracle.

Asset tracking

Cryptocurrencies can be pegged to an external value such as the US Dollar or Gold.

However, it is up to the creator of that cryptocurrency to devise a way to ensure that the cryptocurrency accurately tracks the external asset.

The most common way to do this is to store the external asset and issue only the equivalent value in cryptocurrency coins.

For instance, if Crypto X is pegged to the value of the US dollar, it might store 1 billion USD in its bank and issue out 1 billion USD worth of Coin X.

Storing Cryptocurrencies

Cryptocurrencies are stored in virtual wallets.

Most wallets are designed to only store certain cryptocurrencies. If you have 2 different coins, you will usually need 2 different wallets.

Transaction Mechanics

Transferring of coins are usually cheap and fast.

Wallets and smart contracts submit a transfer request to the blockchain and the miners will add that transaction to it.

Characteristics of Transactions

Irreversible

After a transaction has been confirmed by the miners, it cannot be reversed.

Semi-anonymous

Wallets are not connected to real-world identities.

Fast and Global

Transactions can be completed within seconds or minutes.

This time does not change even if the person you are sending the coins to is physically far away.

You can send coins to anyone around the world

Permissionless

No one can prevent you from setting up wallets, and sending/receiving coins. (You do need access to the Internet though.)

Examples of Popular Cryptocurrencies

• Bitcoin
• Ethereum
• Ripple
• Litecoin
• EOS

Links to Complicated Explanations

• Cryptocurrency – Investopedia
• Cryptocurrency – Wikipedia

Related Terms

• Algorithmic Trading
• Bitcoin
• Market Inefficiencies

The post Cryptocurrency appeared first on AlgoTrading101 Wiki.

An arbitrage happens when an asset is priced differently on 2 exchanges and a trader buys the cheaper one while shorting the pricier one.

Description

There are 3 main methods to execute an arbitrage trade.

First – Betting on Convergence

Buy asset A on exchange X and simultaneously short asset A on exchange Y.

In this trade, we assume that the price of asset A on exchange X and Y will converge eventually.

However, there is a risk that the price does not converge if there are external market constraints – such as an exchange halting fund withdrawals, or certain countries limit currency inflow/outflow.

This requires you to pay an interest cost to short asset A on exchange Y.

Second – Moving Assets

Buy asset A on exchange X, transfer asset A to exchange Y, sell it for a higher price.

The risk here relates to time. If the price difference disappears during the time it takes for the asset transfer to happen, the trader might not be able to sell the asset for a higher (profitable) price on exchange Y.

This requires you to pay a cost to transfer assets between exchanges.

Third – Moving Assets with Insurance

This is a hybrid of the above 2 methods.

Buy asset A on exchange X and simultaneously short asset A on exchange Y.

Next, transfer asset A to exchange Y, close your short trade with the transferred asset.

In this trade, you need to pay both the cost of shorting and asset transfer.

Risk-Free vs Low Risk

Ideally, an arbitrage trade is risk-free. In practice, the risks might be low but is not completely risk-free.

Here are some of the common risks:

• Time risk
• Exchange risk
• Counterparty risk
• Regulation risk
• Country-related risk
• Execution risk
• Liquidity risk
• Default risk

Arbitrage involving more than 2 assets

Arbitrage can occur when there are more than 2 assets. This is common in the Forex markets.

Currency arbitrage in 3 currency pairs occurs when their prices don’t match up.

For instance, if CurACurB = 2 and CurBCurC = 4, CurACurC should be 8.

Thus, if CurACurC is not 8, we can enter an arbitrage trade.

CurA represents Currency A.

CurACurB = 2 means that it takes 2 units of CurB to buy 1 unit of CurA.

Examples

• In mid-2017, cryptocurrencies were priced 30% higher in Korea than in other countries. This is because there was a limitation by the Korean government on capital outflows.
• Gold futures were priced different in COMEX (futures exchange in US) and TOCOM (futures exchange in Japan) in the early 2000s, even after taking into consideration of the currency exchange rate.
• If CurACurB = 2, CurBCurC = 4, CurACurC is 16. We long CurACurB, CurBCurC and short CurACurC.

Links to Complicated Explanations

• Arbitrage – Investopedia
• Arbitrage – Wikipedia

Related Terms

• Statistical Arbitrage (Coming soon)
• Position Sizing (Coming soon)
• Market Inefficiencies

The post Arbitrage appeared first on AlgoTrading101 Wiki.

Alternative data is non-traditional (not price or volume) data that has predictive value in the financial markets.

Description

The 5 popular types of alternative data are:

• Location Data
• Consumer Expenditure Data
• Satellite/Drone Imagery
• Weather Data
• Web-scrapped Data

Alternative data is used by hedge funds and financial institutions to find profitable opportunities in the markets.

Hedge funds either buy these data from data providers or collect them themselves.

Examples of Alternative Data in Action

• Satellite images of Walmart parking lots allow hedge fund to analyze the change in the number of people going to Walmart. This enables them to predict Walmart’s sales figures.

• Surveyors record the number of trucks leaving Company A’s factories. This data allows traders to predict Company A’s revenue.

• Having social media foot traffic data (such as Foursquare’s check-in data) around Restaurant X’s outlets allows traders to predict Restaurant X’s sales figures.

Examples of Alternative Data

• Geolocation data
• Foot traffic data
• Credit card data
• Point-of-Sales data
• Email Receipts
• Satellite Images
• Drone Images
• Search Traffic
• Job Listings
• Web Reviews
• Rainfall Levels

Links to Complicated Explanations

• Alternative Data – Wikipedia

Related Terms

• Machine Learning
• Algorithmic Trading Strategies
• Market Inefficiencies

The post Alternative Data (Finance) appeared first on AlgoTrading101 Wiki.