ShareDigest blog

Historical Data

There are numerous sites offering various levels of quality historical stock price data. Below you can find some common sources of free historical data. Google is also your friend here, there’s lots of datasources out there.

Our aim will be to minimize costs

Using Indexing To Speed Up MySQL

The exact workings of indexes will not be discussed here (for a more complete discussion of how they work, see http://dev.mysql.com/doc/refman/5.1/en/mysql-indexes.html )

What we’ll also be looking at co-incidentally is how MySQL stored procedures can be used to populate tables quickly and easily with data (in this case random data).

Demo of Index Performance

In order to see how much indexes can improve query performance across multiple tables, we shall conduct a simple test involving three tables, t1, t2, and t3 with each table containing a column i1,i2 and i3 respectively and each having 1000 rows that are populated as follows: column i1 has rows with values 1 – 1000, i2 random values in range: , and column i3 the same as i2 (but different values).

The following MySQL procedure achieves the above by creating and populating the tables as needed. Simply import it from your Linux command line as you would any sql file:

The pop_random.sql file can be found here, exposing the init_tbl(’tablename’, no_random_entries) procedure call to us.

DELIMITER $
DROP PROCEDURE IF EXISTS init_tbl $
CREATE PROCEDURE init_tbl(IN tbl CHAR(64), IN upper INT)
BEGIN
	DECLARE count INT;
	SET count = 1;
 
	-- rem all first
	SET @s := CONCAT('DELETE FROM ', tbl, ' WHERE 1');
	PREPARE stmt FROM @s;
	EXECUTE stmt;
 
	-- Loop from 1 to upper--
	WHILE count < (upper + 1) DO
		SET @s := CONCAT('INSERT INTO ', tbl, '(i0, i1,i2,i3) VALUES (',count, ', 0, 0, 0)');
		PREPARE stmt FROM @s;
		EXECUTE stmt;
		SET count = count+1;
	END WHILE;
 
	CALL pop_random(tbl);
END $
 
DROP PROCEDURE IF EXISTS pop_random $
CREATE PROCEDURE pop_random(IN tbl CHAR(64))
BEGIN
	SET @r := 0;
	SET @s := CONCAT('UPDATE ', tbl, ' SET ', tbl,'.i1= (@r := @r + 1) order by rand()');
	PREPARE stmt FROM @s;
	EXECUTE stmt;
 
	SET @r := 0;
	SET @s := CONCAT('UPDATE ', tbl, ' SET ', tbl,'.i2= (@r := @r + 1) order by rand()');
	PREPARE stmt FROM @s;
	EXECUTE stmt;
 
	SET @r := 0;
	SET @s := CONCAT('UPDATE ', tbl, ' SET ', tbl,'.i3= (@r := @r + 1) order by rand()');
	PREPARE stmt FROM @s;
	EXECUTE stmt;
 
	SET @msg := CONCAT(tbl, ' successfully created and populated.');
	SELECT @msg;
 
END $
 
DELIMITER ;
 
DROP TABLE IF EXISTS `t1`;
CREATE TABLE `t1` (
  `i0` int(11) NOT NULL,
  `i1` int(11) NOT NULL,
  `i2` int(11) NOT NULL,
  `i3` int(11) NOT NULL,
  PRIMARY KEY  (`i0`)
) ENGINE=MyISAM DEFAULT CHARSET=latin1;
 
DROP TABLE IF EXISTS `t2`;
CREATE TABLE `t2` (
  `i0` int(11) NOT NULL,
  `i1` int(11) NOT NULL,
  `i2` int(11) NOT NULL,
  `i3` int(11) NOT NULL,
  PRIMARY KEY  (`i0`)
) ENGINE=MyISAM DEFAULT CHARSET=latin1;
 
DROP TABLE IF EXISTS `t3`;
CREATE TABLE `t3` (
  `i0` int(11) NOT NULL,
  `i1` int(11) NOT NULL,
  `i2` int(11) NOT NULL,
  `i3` int(11) NOT NULL,
  PRIMARY KEY  (`i0`)
) ENGINE=MyISAM DEFAULT CHARSET=latin1;
 
-- _initialize the tables
call init_tbl('t1', 1000);
call init_tbl('t2', 1000);
call init_tbl('t3', 1000);

Running the above script will give us the three tables each with 3 columns i1-i3, each with 1000 lines of random, unique numbers. Stored procedures can make it easy to keep common procedures in SQL grouped together and perform complex operations. For example, if you’d like to populate the table ‘t1′ with more rows, simply run the query:

call init_tbl('t1', 20000);

Getting back to indexing, we’ll focus on the following three columns: t1.i1, t2.i2 and t3.i3. We write the following query to find all combinations of table rows in which the values are equal:

SELECT t1.i1, t2.i2, t3.i3 FROM t1 INNER JOIN t2 INNER JOIN t3 WHERE t1.i1 = t2.i2 AND t2.i2 = t3.i3;

That query took 2 min 5.36 sec to finish – quite a while! By running this query without using indexes, we have to scan ALL rows to determine which rows contain which values. So all combinations must be tried to find the matches on the WHERE clause. The largest possible number of combinations is: 20,000 x 20,000 x 20,000 – a very large number! That’s alot of wasted effort. As the tables grow, the processing time increases even more in the absence of indexes, leading to poor performance. By using indexes, performance can be greatly improved due to the query being processed as follows:

1. Determine the value of the first row from table t1;
2. Now, using the index on t2, immediately find the value that matches the value from the 1). (Similarly, an index on t3 will allow the value from t2 to be found immediately).
3. Go to the next row of t1 and repeat steps 1) – 2), until all rows in t1 have been processed.

So the above procedure still does a full scan of table 1, but indexed lookups on t2 and t3 will cause the query to run many many times faster. Lets create indexes on the tables and run our above query again to evaluate performance improvement:

#Do same for t3.i3
 ALTER TABLE `t2` ADD INDEX ( `i2` ) ;

Now running our select & join query, produces the same output as before, but in 0.22 secs – a speed increase of roughly 568 times. This increase will be even more the larger the tables are.

How To Decide Which Columns To Index

Day 1:

Feeling mega-hungry. The fact that I went for a surf and a run probably didn’t help. Friend informed me I am not having enough juice.

Day 2:

Going much better, got tonnes more juice this time.

Will keep this post updated …

Day 7:

Ok So I’ve skipped a few days (of posting, not of taking the juice!) and while I am pretty sick of the juice, I can without a doubt say that I have lost several kilograms and feel fantastic and more energetic than ever. OK going to go get me a big fat juicy steak!

Highly recommend the diet, if only for its weight-loss powers!

SD

So you want to get started in algo-trading huh? While the start of an algo-trading operation may seem like a daunting task, it can be possible to beat the institutional traders at their own game (or at least share in the profits), have fun and learn an immense about trading the markets autonomously in the process. That is what this post will be about, searching for quantitative strategies and a systematic approach to starting an algorithmic trading operation.

What this post will aim to do is explore the journey of the trading business startup, from finding a viable trading strategy, backtesting it for verification on historical performance, setting up the tech-infrastructure (low-cost!) and building and deploying an automated trading system (ATS) to execute your strategy.

Other than that we’ll be looking at using Matlab or using its cheaper alternative, R for the backtesting of some quantitative trading strategies.

Being proficient at basic stats and basic programming will definitely help, but you won’t need a PhD to put the theories into practice. Strategies will mostly be of the quantitative nature e.g. statistical arbitrage, and the aim is to keep things simple for now. StatArb deals with the simplest financial instruments: stocks, futures and sometimes currencies.

Let’s look at the process of developing a successful trading strategies and putting it into action!

Finding A Quantitative Strategy That Works (And Suits You!)

Unlike popular opinion would suggest, many strategies are publicly available and while they may not be super-profitable can be tweaked to become profitable.

The key is to backtest strategies for historical performance, thereby verifying that they represent a sound hypothesis. While this alone does not guarantee a profitable strategy, it is certainly a very good indication on the future success of a strategy.

Key things to consider when deciding on a suitable strategy are:

• Available time: If you’re working full time and only able to trade part time, you probably don’t want to trade intraday strategies but rather stick to ones that trade overnight. Quantitative trading lends itself well to automation in this regard, since the computer can handle most things automatically, if set up correctly.
• How programmatically inclined you are: Knowing some languages like Java, C# or C++ will definitely be a huge plus when it comes to algo-trading (after all its not called ‘algorithmic trading’ for nothing – trading algorithms are just computer programs firing off buy and sell orders to the markets.
• Trading capital: Depending on your level of available capital, you may not be in a position to trading algos in the first place. Your capital will determine alot of things such as what type of accounts you’ll be setting up, brokerage discounts (based on volume traded), etc, etc. Generally, the more you have to begin trading with, the better.
• Does it have a high Sharpe ratio?
• Does it outperform a benchmark? (Say the ASX/S&P200 Index)
• Does it have small enough drawdown and short enough drawdown duration?
• Does the strategy’s historical performance start declining towards the more recent years compared to that of the earlier years?

Its All About The Odds…

Las Vegas. Great food, show girls, and a multi-billion dollar gambling
business. The money made by the casinos is only matched by the profits
on Wall Street. And the profits of both are based on mathematical probabilities.

What does this have to do with trading systems?

• We want the odds of a trade to favor us – We want a positive expectancy
• We want a lot of trades – opportunity
• We want turn over so we can compound the profits – holding time.

What we as traders must do is become the house. The odds in our trading
must favor us, we need a reasonable number of trades during the year
and the trades must be completed in a reasonable amount of time for
compounding to be effective.

Expectancy is simply the product of your profit percentage per win and
your win rate minus the product of your loss percentage per loss and
your loss rate, or:

Expectancy, E = (Ave. Win Size * % Wins) – (Ave. Lose Size * %
Losers)

For example, if we have the results of backtesting a purely
technical system, with the following results:

• Win percentage 6%
• Win rate 60%
• Loss percentage 4%
• Loss rate 40%

The expectancy is 2.0% per trade, or (6% x 60%) – (4% x 40%).

That means, on an average trade, 2% of the money traded is yours to
keep. That’s better odds than a casino gets on blackjack. Now,
that may not sound like a lot of money. If your average trade is $10,000
– 2% is $200 profit per trade. If you have 300 trades per year,
then you have a $60,000 profit per year with an average trade of only
$10,000. This does not even include the profits if you compounded
the average trade.

If you explore the expectancy formula, you will notice that there is
no one set of numbers that could give a positive expectancy but an infinite
number of sets and therefore an infinite number of trading systems that
could be profitable.

Given that, it is possible to develop systems where the stop-loss is
larger than the profits. The stop-loss is academic, as long as your
profit expectancy is positive.

Here’s another example: we could use a 20% stop loss and a 5%
profit target and come out with the same exact 2% expectancy as long
as my win rate is high enough! An 88% win rate in this example would
yield 2.0%, the result of (5% x 88%) – (20% x 12%).

Or, you could arrive at a positive expectancy with a very low win rate.
One of the more famous expectancy numbers comes from William O’Neil,
advocate of the CANSLIM system and founder of Investors Business Daily.
If we use his stop and target numbers of 8% and 20% and his published
win rate of 30%, the expectancy can be calculated to be: (20% x 30%)
- (8% x 70%) or +0.4%.

The bottom line is: expectancy must be positive if you want to make
a profit over time. Never use a system with a zero or negative expectancy.
You will not win. You cannot beat the house over a long series of bets
or trades. Be the “House”.

No matter what your expectancy is, you will not make a great deal of
money unless you have a lot of opportunities to trade. Again, the casino
analogy. The casino may only make 1-2% per hand of blackjack, but they
turn over those hands very quickly – 30 to 40 hands per hour.
Play blackjack long enough and you will lose over 40% of your money
per hour! No wonder they can offer those wonderful comps.

The most overlooked area of trading is the “holding period.”
In order to make money, you must have a system that generates a positive
expectancy and a lot of opportunities. But you must have access
to your money. If your trade’s hold time is too long, you
can’t take advantage of all or even most of your opportunities. Your
trading money or buying power is always tied up because you have to
wait too long to close your trades.

Casino analogy time. If the house odds in Blackjack are 2.5%, that means
for ever $2 bet, the casino makes, on average, 5 cents. If you only
play 1 game per hour, the casino makes 5 cents per hour. If you play
60 games per hour, the casino has all of your $2 in 40 minutes. All
things being equal, the game with the fastest turnover is the more profitable
for the casino.

It’s no different with trading. You will be more profitable with $100,000
that you could “turn” 250 times per year, than $500,000 that
was tied up in one trade for 12 months. As an example, let’s say we
have one trade and that trade yielded a 50% return. You just had a great
year – a $250,000 profit.

On the other hand, say you had $100,000 for stock purchases, and your
expectancy was only 1.2% per trade but you turned over your stocks 250
times in the same year. This method ends up generating $300,000 for
the year, and that assumes you never increase the position size as the
equity grows. You just had a better year. And it is easier to get 1.2%
per trade than 50%.

The bottom line for a great bottom line is:

• A positive expectancy
• A good number of trades
• A short holding period

The Stock Market

The stock market is essentially the term given to the place
where traders and investors can buy and sell stocks through
agents known as brokers. This takes place in a very similar
fashion to that of a cattle market or any other livestock market for
that matter, in that prices vary on a constant basis according to
supply and demand.

The Stock Exchanges

The places where stocks and other securities such as options and
futures are traded is known as an exchange. There may only
be a single exchange making up the stock market for a country,
or in some cases there may be numerous exchanges within one domestic
market. For example, in the US there are three main exchanges:

• The American Stock Exchange (AMEX)
• The New York Stock Exchange (NYSE); and
• The National Association of Securities Dealers Automated Quotes
  (NASDAQ).

There are also a number of regional exchanges including the Chicago
Stock Exchange (CHX) and the Pacific Stock Exchange (PSE) in addition
to the three main exchanges above.

Some major exchanges in other worldwide markets include:

Australia

The Australian Securities Exchange
(ASX)

Great Britain

The London Stock Exchange
(GBX)

India

The National Stock Exchange of
India (NSE)

The Bombay Stock Exchange (BSE)

China

Hong Kong Stock Exchange (SEHK)

Shanghai Stock Exchange (SSE)

Introduction To Futures

A futures contract is a commitment to deliver or
receive a standardized quantity and quality of a commodity (or financial
instrument) at a specified future date. The price associated with
this trade is the trade entry level.

• The futures contract is standardized
  - this means the farmer doesn’t have to find a specific buyer.
• The transaction can be executed nearly instantaneously
  with a one-line phonecall order (e.g. “Sell 2 March grain
  at the market”).
• The cost of the trade (commissions) is minimal
  compared with the cost of an individualized forward contract (ie.
  if the farmer were to sell to one specific buyer in the future
  via a “contract”).
• The farmer can offset his sale at any time between
  the original transaction date and the final trading day of the
  contract (the reason why he’d want to do this will be discussed
  shortly).
• The futures contract is guaranteed by the exchange.

Until the early 1970’s, futures markets were restricted to commodities
such as wheat, sugar and copper. Three additional market sectors
have been added to the futures area since then: currencies,
interest rate instruments, and stock exchanges.
The same basic principles apply to these non-commodity futures markets.
Trading quotes (futures contract prices) represent prices for a
future expiration date, as opposed to current market prices. These
new financial markets have witnessed spectacular growth since their
introduction and account for approximately two thirds of all trading
volume. In this sense, futures appear to be a far more appropriate
designation than commodities, although the term “commodities”
is still often used when referring to futures (they are considered
synonymous).

Delivery

Shorts who maintain their positions after the last trading day are obligated to deliver the actual commodity (or financial
instrument) against the contract. Similarly,
longs who maintain their position after the last trading day must accept delivery.
In the commodity markets, the number of open long contracts is always equal to the
number of open short contracts (see the Volume & Open Interest section). Most
traders have no intention of making or accepting delivery, and hence will offset
their positions before the last trading day. (Longs offset their position by entering
a sell order, shorts by entering a buy order).

Only a very small fraction of open contracts actually result in delivery. Since
the early 1980s, there has been a strong settlement toward using a cash settlement
(ie. outstanding long and short positions are offset at the prevailing price level
at expiration) instead of a delivery procedure. Most financial contracts and some
commodity-type markets use cash settlement.

Volume & Open Interest

Volume is simply the total number of contracts traded
on a given day. Volume figures are available for each traded month in
a market, but most traders focus on the total volume of all traded
months.

Open interest is the total number of outstanding long
contracts, or equivalently, the total number of outstanding short contracts.
In futures markets, the two are always equivalent. When a new contract
begins trading (normally about 9 – 18 months before its expiration date),
its open interest is equal to zero. If a buy order and sell
order are matched, then the open interest increases to 1. Basically,
open interest increases when a new buyer purchases from a new seller,
and decreases when an existing long sells to an existing short. The
open interest will remain unchanged if a new buyer purchases from an
existing long or a new seller sells to an existing short.

Volume and open interest are very useful as indicators
of a market’s liquidity. Not all listed futures markets are actively
traded. Some are virtually dead, while others are borderline cases in
terms of trading activity. Illiquid markets should be avoided,
because the lack of an adequate order flow will mean that the trader
will often have to accept very poor trade execution prices if he wants
to get in or out of a position.

The breakdown of volume and open interest figures by contract
month can be very useful in determining whether a specific month is
sufficiently liquid. For example, a speculater wishing to enter a long
position may prefer the futures contract with an expiration date nine
months forward, as opposed to more nearby contracts, because he believes
the forward position is relatively underpriced. The most important reservation
about trading the more distant contract would be whether its level of
trading activity was sufficient to avoid problems related to illiquidity
(poor execution prices). In this case, the breakdown of volume and open
interest figures by contract month can help the speculator decide whether
its reasonable to enter a position in the more forward contract or if
its better to restrict trading to the nearby positions.

Contract Specifications

Futures are traded for a wide variety of markets on a
number of exchanges both in the United States and abroad. Although we’ll
not provide a full list of available futures markets and their exchanges,
an example market will be provided:

(…continued)

1. Exchange

The exchange is where the market is being traded. In this case we have
wheat being traded on the Chicago Board of Trade (CBT).
Among the other US futures exchanges are the Chicago Mercantile Exchange
(CME), New York Futures Exchange (NYFE) and the Financial Instruments
Exchange (FINEX) to name but a few. Foreign exchanges include the Deutsche
Terminboerse (DTB), International Petroleum Exchange of London (IPE),
Marche a Terme International de France (MATIF).

2. Trading Hours

As indicated, trading hours are listed in terms of the local times
for the given exchange (All US exchanges are currently located in either
the Eastern or Central time zones).

3. Contract Size

The specification of a uniform quantity per contract is one of the
key ways in which a futures contract is standardized. By multiplying
the contract size by the price, you can determine the dollar value of
the contract. For example, if wheat is trading at $3.00/bushel (bu.),
the contract value equals $15,000. Although there are a few important
exceptions, roughly speaking, higher per-contract dollar values will
imply a greater reward / risk level. (The concept of a contract has
no meaning in the interest rate markets).

4. Months Traded

The code for the monthly symbols is shown in the table above. Each
market is traded for specific months. For example, corn is traded for
March, May, July, September and December. The last trading day for a
contract will occur on a specified date in the contract month or, in
some cases, the month preceding the contract month. For most markets,
futures are listed for contract months at least one year forward from
the current date. However, trading is usually heavily concentrated in
the nearest one or two contracts.
5. Price Quoted In

Indicates the relevant unit of measure for the given market.

6 . Minimum Fluctuation

This column indicates the minimum increments in which
prices can trade. For example, the minimum fluctuation for wheat is
1/4 c/bu.. This means you can enter an order to buy December wheat at
$3.01 1/2 or $3.01 3/4, but not $3.01 5/8 per bushel.

7. Value of Minimum Fluctuation

This figure is obtained by multiplying the minimum fluctuation
by the contract size. For example, for wheat, 1/4 c/bu. x 5,000 = $12,50.

8. Maximum Daily Limit

Exchanges normally specify a maximum amount by which the
contract price can change on a given day. For example, if the December
wheat contract closed at $3.10 the previous day, and the daily price
limit is 20c/bu., wheat cannot trade above $3.30 or below $2.90. Some
markets employ formulas for increasing the daily limit of a specified
number of consecutive limit days.
This has quite a profound effect upon us as traders: In
cases in which free markets would normally seek an equilibrium price
outside the range boundaries implied by the limit, the max-limited futures
market will simply move to the limit and virtually cease to trade. For
example, if after the market close the U.S. Department of Agriculture
(USDA) releases a very bullish wheat crop production estimate, which
hypothetically would result in an immediate 30c/bu. price rise in an
unrestricted market, prices will be locked limit up (20c/bu.)
the next day. This means that the market will open and stay at the limit,
with virtually no trading taking place. The reason for the absence of
trading activity is that the limit rule restriction maintains an artificially
low price, leading to a large surplus of buy-orders at that price but
few if any sell orders.
In the case of a very sever surprise event (e.g. sudden
major crop damage), a market could move several limits in succession.
If this occurs, traders on the wrong side of a trade might not able
to liquidate their positions until the market trades freely! The
new trader should be aware of and practice caution to this possibility.
However there’s no need for overly frightened, since such events of
extreme volatility rarely come as a complete surprise. In most cases,
markets vulnerable to such volatile price action can be identified early.
Some examples of such markets would include commodities in which the
USDA is scheduled to release a major report, coffee or frozen concentrated
orange juice during their respective freeze seasons, and markets that
have exhibited recent extreme trading volatility.

9. First Notice Day

10. Last Trading Day

This is the last day on which the positions can be offset
before delivery becomes obligatory for shorts and the acceptance of
delivery obligatory for longs. As indicated previously, the vast majority
of traders will liquidate their positions before this day.

Options vs. Other Instruments

Option trading provides many advantages over other plain stock trading. Leverage,
limited risk, insurance, profiting in bear markets, each-way betting or market going
nowhere are only a few. But let’s look at a couple in more detail.

Leverage

One thing to note before we go on is that the buyer of an options contract pays
an amount, known as the premium, to the option seller. An option seller is also
known as the writer of the option. The option premium is simply the amount paid
for the option.

Options give the buyer the right to buy a number of shares of the underlying instrument
from the option seller. The amount of shares (or futures contracts) to buy is determined
by;

The number of option contracts, multiplied by the contract multiplier (also called
contract size) is different for most classes of options and is determined by each
exchange. In the US, the contract size for options on shares is 100.

This means that every 1 option contract gives buyer the right to buy 100 shares
from the option seller.

So, if you buy 10 IBM option contracts, it means that you have the right to buy
1,000 IBM shares at expiration if the price is right (10 x 100).

Note: In other countries such as Australia, the contract multiplier for stock options
is 1,000, which means the every option contract you buy entitles you to 1,000 underlying
share contracts. So pay attention to the contract specs before you begin option
trading.

This also means that the price of the option is also multiplied by the contract
multiplier. For example, say in the above you purchased 10 options contracts that
were quoted in the marketplace for 15c, then you would actually pay the seller $150.

This is a crucial concept to understand. If you go out and buy 5 IBM share options
for 15c that have a Strike Price of $25, then you will;

Pay the option seller $75

If you decide to exercise your right and buy the shares, you will have to buy 500
(5 x 100) (100 being the contract size) shares at the exercise price of $25, which
will cost you $12,500.

In this case, your initial investment of $75 has given you $12,500 exposure in the
underlying security.

Option trading is very attractive for the small investor as it gives him/her the
opportunity to trade a very large exposure whilst only outlaying a small amount
of capital.

Say you bought a $25 call option for $1 while the underlying shares were trading
at $26. If the market rallies to $27 the option must at least be worth $2 because
you can exercise your right at $25. So, even though the shares only went up 3.8%
you DOUBLED your money because you can now sell back the option for $2.

Penny stocks are also known to carry this type of risk/reward profile. Penny Stocks
are companies that have very low share prices. You can buy some stocks for as little as 10c. It is much more common for a penny stock to trade from 10c to 20c than it
is for Microsoft to trade from $25 to $50!

For this reason penny stock trading is becoming very lucrative for online speculators.
They can still trade the stocks outright as well as making massive returns if they
are correct about their view on market direction.

The only drawback with penny stocks is trying to pick which stocks to buy. I’m not
that familiar with trading penny stocks, however, I know of a great site that provides
stock picks for penny stocks every two weeks – <penny stock affiliate link>.
They have a free trial, so you can see for yourself whether penny stock trading
is for you or not.

Penny stocks can be risky though – there’s a reason why they’re so cheap, nobody
wants them! So, be careful to act on the right information.

Limited Risk

Remember our initial example of Peter buying a Microsoft Call option? Here are the
details of that trade provided with the appropriate jargon;

Underlying: MSFT

Type: Call Option

Position: Long (i.e. bought the contract)

Strike Price: $25

Expiry Date: 25th May

At the time of the trade, Microsoft shares (the Underlying) were trading around
$30. The Call option contract had been valued and was trading at $6.5 – known as
the premium, but more on this under pricing.

So, from the above information we can conclude that after the 25th May, if Microsoft
is trading above $31.50 we can make a profit on this.

Why $31.50? Because we paid $6.50 for the right to have this option in the form
of a premium to the option seller. This means we must consider this in our profit
estimate. Therefore we add the option premium to the strike price to determine our
break even point.

A Profitable Trade

If Microsoft shares are trading at $40 by the 25th May, then we will elect to exercise
our right to Call the shares from the option seller. Then we will be assigned Microsoft
shares at the exercise price of $25, which is the same as if we actually bought
Microsoft shares for $25.

Note: If we exercise our right and take delivery of the shares, this means that
we will have to pay the full amount for the shares. So, the number of option contracts
bought multiplied by the contract size multiplied by the exercise price. If you
are planning to hold onto option contracts until expiry and take delivery, make
sure you have the cash!

But, they are now trading at $40 at the stock exchange! So, you have Microsoft shares
in your trading account with a purchase value of $25, yet they are trading at $40.
So, you can sell them at $40 and make $8.50 per share.

Why $8.50? Remember the premium we paid? We have to consider that with our profit
estimate.

Think about what happens as the underlying price continues to rise. You continue
to make more and more money once the stock price has exceeded the strike price.

But what about the downside risk?

A Losing Trade

Let’s imagine at expiration Microsoft shares are trading below our exercise price
of $25 at, say, $20. Will we decide to exercise our right and take delivery of the
shares and pay $25 per share? No way, because they’re only worth $20. So, we will
just do nothing and let the option contract expire worthless.

What have we lost though? We lose the premium that we paid to the seller, which
in this example was $6.5. That’s it. A lot less than if the stock plummeted and
we lost our entire investment.

What about if there is a stock market crash and Microsoft Shares are trading at
$5 at the time of expiration? The same as if the shares are trading at $20 – nothing.
We just let the option contract expire worthless and lose our premium – $6.5.

Limited Risk AND Unlimited Profit Potential

Can you see now how this type of strategy gives you the best of both worlds – both
limiting your risk and at the same time leaving you open to make unlimited profit
if the market rallies?

Not all option strategies have this payoff benefit. Only if you are buying options
can you limit your risk. For option sellers, this is the reverse – they have unlimited
risk with limited profit potential.

So, why would anybody want to sell options? Because options are a decaying asset,
which you can read more about under the Time Decay section.

Insurance

Another reason investors may use options is for portfolio insurance. Option contracts
can give the risk averse investor a method to protect his/her downside risk in the
event of a stock market crash.

Put Options

Call Options

1. The underlying stock;
2. The Expiration Date; and
3. The Strike Price.