Preview of Forex trading

It doesn’t matter WHAT you trade - Stocks, Options, Futures, Forex, this groundbreaking FREE consumer guide shows you how to become a better trader.

It’s true… I just spoke with the developer of a trading course designed to flourish in any market and any time frame, and he verified that for a short time he’s giving away the first 5 chapters of this step-by-step, A to Z trading ’secrets’ course for the first time.

Let’s be honest. If you’re like most traders, you’ll probably just forget about this message and not take action on it. But in my opinion, it’s the smart traders that take that extra 15 seconds throughout their day to acquire extra trading knowledge wherever they can get it. What’s it going to cost you? 15 seconds? Is that a good deal or a GREAT deal?

Trading Secrets

The good news is that if can ‘pull the trigger’ and go to the link below, you’re already ahead of most traders that just won’t take the time to acquire new knowledge. Here’s a summary of what those smart traders will get with my compliments in this 5 chapter trading preview…

** Introduction

** The Top 11 Trading Myths

** The Bad News & The Good News

** Tools

** Trading Indicators & Methods

Go here now to get this complimentary ’sneak peek’:

Trading Secrets

And also, you’re also going to get 3 extra bonus eBooks that you can add to your personal wealth archive.

** Discovering Stock Trading’s Winning Edge

** Choosing a Trading System That Actually Works

Commodity Forex Online Trading - get a slice of 2 trillion USD

At 2 trillion dollars worth of trades each and every day, the Commodity Forex Online Trading market is the single and biggest financial trading institution worldwide. Also known as Forex, the Commodity Forex Online is referred to as FX, Spot FX or even Spot.

Ever wondered how big the Commodity Forex Online Trading market is, be ready to be astounded. By comparison, the New York Stock Exchange is a light weight as it “only” trades an average of 2 billion dollars a day. In fact, you would need to combine both the Futures market and the Stock market and then time it by 3 to get nearer to the value of the Forex Trading Market. Did you ever think it was so big?

Do you also want to find out what is traded in Forex Trading, well, to the risk or appearing simplistic, the answer fits in one word! Money! Lots and lots of it! Forex Trading happens when one currency is exchanged for another and since only one currency can ever only be exchanged against another, the exchange is referred to as pairs. As in Euro dollar for US dollar (EUR/USD) or the British pound for Japanese Yen (GBP/JPY).

Before money had even been invented, the economy relied on the simple principle of barter exchange. If you wanted one product, you would pick one of yours and hope to exchange it with the one you liked. In this case, the owner of the other product and yourself would set the price and if agreed, the exchange would take place. When it comes to Commodity Forex Online Trading, the analogy with the bartering system still holds, only this time, the value of the currencies is floated and thus estimated globally according to market forces.

Forex Trading is for Everyone.

Foreign exchange rates for currency have created a multi billion dollar market for the trade of currency alone. Forex trading as it is called, exceeds 3 trillion dollars daily USD. Because it is done accross the world, it is the largest trading system in existence.

The forex trading system is different than the traditional stock market in that not everyone has the same price per share or pip. The difference comes from a large gap between what governments and larger banks can afford to trade compared to the average retail forex trader. A system of levels or tiers has been put in place and prices per share are based on the amount bought or sold. The larger the amount, the better or less difference in price between the sell and buy pricing that there is.

There is little regulation though between countries because the trading is done across boarders and by many different levels of people. There is an existing practice in place, however, that allows for international agencies to track forex currency trading amounts in order to squash potential exploiters. With trades not being based on one dollar amount, but by a spread of amounts based on where the trade originates from, there is a possibility for someone to exploit the price.

With governments, central banks, and currency speculators involved in the forex trade, a system has been worked out to keep it a bit more fair. One big difference between the regular stock market and the forex market is that not all traders have access to the same prices due to the volume that some traders have access to.

Controlling Your Forex Investment

Even though investing can be fun and exhilarating the young investor must understand that there are some very basic rules that need to be followed. Making money can be extremely fun, but loosing money can sometimes set you back in life several years, not allowing you to be able to invest any more. Let’s take a look at one simple aspect that many people forget while investing; Control

This is something that I learned later on in my investing career. When I first started I didn’t care who was in control I just wanted my money out there in an investment earning more interest then the bank was paying. I thought that the returns would stay high as the previous years, and that the moment things changed my broker would call me and suggest changing markets. I was nave to think that other people would care for my money the same way that I would. This was a painful lesson.

As I started investing my father would always tell me “No one cares as much about your money as you do”. I had invested with a friend and I automatically thought that he would put my interest first, and for the most part he did. It was inappropriate for me to think that he would care and look over my money as I would. Why? It wasn’t his! He hadn’t sweat, and worked hard for that money. Knowing this now, I take all control of my money as I know that I am the only one who will care for it as I will.

Education Comes Before Riches in Forex Trading

All of us dream about the potential of working at home make trades through the internet a few hours a day and making enough money to quit our day job. In all actuality this is quite feasible for any one of us who is determined to be successful in trading in the Forex market.

I want to give you a little bit of advice before you jump in and start trading in the Forex market. I think that this advice will prove to be very valuable to you in the long run. I know that I wish I would have listened to this advice before my first investment. I would probably be a whole lot richer today then I really am.

The greatest investment that anyone can invest in is our brain. That’s right, in our education and knowledge that we have as an investor. Books are cheap, many times free. Most of us also have “free time” that we can spend to read these books. So, why don’t we do more reading before we start investing?

Emotion is definitely one aspect that causes us to jump right into investing without first studying and preparing for that investment. We read articles, and testimonials of people making so much money without doing anything that we think we are going to be that profitable as well. This isn’t always true. But we can see ourselves driving that new car that we bought from our investment earnings and we just can’t wait. Emotion is a very dangerous thing in the world of investing.

Market Direction (GDP Growth and the American Consumer)

If the ultimate cause of the economic crisis was an excess of credit its cure will almost certainly be a contraction of debt. Banks have already tightened their lending standards, mortgage companies are again requiring 20% down on homes loans, and credit card firms have stopped flooding consumers with unsolicited cards. Banks are not lending because they need to support their balance sheets; consumers have limited their use of credit to repair their own household accounts.

It is consumer spending and borrowing that is the occasion for much of the business of lending. Bank balance sheets can be clean and strong, but if consumers decline to borrow or borrow only at much lower overall amounts it will not matter what condition the financial system is in. If loan demand shrinks then the consumer credit sector will remain moribund and consumer spending will not fuel an economic recovery.

It is not only jobs and unemployment that is restraining spending. If the consumer mindset that sought high leverage as part of an expansive lifestyle is lost then no amount of economic stimulus by Washington will bring it back.

After all what is the stimulus package about except encouraging consumption? And it was precisely that attitude of unsupportable spending across housing and consumer credit that produced this economic mess. Axiomatic maybe, but if no one had bought sub-prime housing Wall Street and Fannie and Freddie could not have created the financial debacle. If the consumers’ attitude towards consumption has changed then all Washington deficit spending in the world will not recover the economy.

Economic shocks, recessions and especially depressions have psychological consequences that endure. My parents lived though the Depression and remembered that time of trouble all their lives. Neither of their fathers were ever out of work, neither of their families lost their homes or bank accounts but their generation’s notions on spending, the stock market, saving, debt and consumption were formed by the Depression. They never trusted the stock market, their only debt was the mortgage on their house which they saved to pay off early and they shunned almost all kinds of self pleasing spending indulgences.

Will this recession produce such a change in consumer outlook? The answer probably depends on how long it lasts. One of the salient facts of the Depression was its length. For a full decade, from 1930 to 1940, unemployment in the US never dropped below 14%, for much of the time is was considerably higher. The enduring difficulty of that decade burned itself into the psyche of a generation.

Personal checks from Washington do not produce consumption. At best the government can replace some jobs lost from the private sector and create a temporary boost to GDP. Citizens spend freely because they have confidence in the future and in their future income. So many institutions have been damaged or found to be faulty in this crisis that confidence in the US economic system itself has likely been damaged. Government also, the lender of last resort, is operating only as a stopgap. Wary unconfident consumers are not about to open their wallets and plunge in a new SUV.

How much will the events of the past two years alter consumer attitudes? Are the empty malls and restaurants tied only to current conditions or a permanent change? Is the level of overall American of consumer goods headed down? It is difficult to predict a return to free spending ways of the past decade but the longer the recession lasts the more pronounced will be the change in consumer habits.

It is likely that anyone who has gotten burned in this economic collapse will not easily return to their prior spending habits.

Since that is almost everyone the change in long term GDP growth rates may ultimately be profound.

Trend Lines

A trend line is formed when you can draw a diagonal line between two or more price pivot points. They are commonly used to judge entry and exit investment timing when trading securities.

A trend line is a bounding line for the price movement of a security. A support trend line is formed when a securities price decreases and then rebounds at a pivot point that aligns with at least two previous support pivot points. Similarly a resistance trend line is formed when a securities price increases and then rebounds at a pivot point that aligns with at least two previous resistance pivot points. The following chart provides an example of support and resistance trend lines:

Trend lines are a simple and widely used technical analysis approach to judging entry and exit investment timing. To establish a trend line historical data, typically presented in the format of a chart such as the above price chart, is required. Historically, trend lines have been drawn by hand on paper charts, but it is now more common to use charting software that enables trend lines to be drawn on computer based charts. There are some charting software that will automatically generate trend lines, however most traders prefer to draw their own trendlines.

When establishing trend lines it is important to choose a chart based on a price interval period that aligns with your trading strategy. Short term traders tend to use charts based on interval periods, such as 1 minute (i.e. the price of the security is plotted on the chart every 1 minute), with longer term traders using price charts based on hourly, daily, weekly and monthly interval periods.

However, time periods can also be viewed in terms of years. For example, below is a chart of the S&P 500 since the earliest data point until April 2008. Please note that while the Oracle example above uses a linear scale of price changes, long term data is more often viewed as logarithmic: e.g. the changes are really an attempt to approximate percentage changes than pure numerical value. If we were to view this same chart linearly , we would not be able to see any detail from 1950 to about 1990 simply because all the data would be compressed to the bottom.

Trend lines are typically used with price charts, however they can also be used with a range of technical analysis charts such as MACD and RSI Trend lines can be used to identify positive and negative trending charts, whereby a positive trending chart forms an up sloping line when the support and the resistance pivots points are aligned, and a negative trending chart forms a down sloping line when the support and resistance pivot points are aligned.

Trend lines are used in many ways by traders. If a stock price is moving between support and resistance trendlines, then a basic investment strategy commonly used by traders, is to buy a stock at support and sell at resistance, then short at resistance and cover the short at support. The logic behind this, is that when the price returns to an existing principal trendline it may be an opportunity to open new positions in the direction of the trend, in the belief that the trendline will hold and the trend will continue further. A second way is that when price action breaks through the principal trendline of an existing trend, it is evidence that the trend may be going to fail, and a trader may consider trading in the opposite direction to the existing trend, or exiting positions in the direction of the trend.

Practice Account

Forex practice accounts allow you to trade the forex market while not putting your hard earned capital at risk. These accounts are often also called forex demo accounts, these accounts should be free - so if a forex broker is trying to charge you for one – just say no thank you and look for another broker.

Most forex practice accounts will work for about 30 days, some are longer and some are shorter it all depends based on the forex broker that you choose to open your practice account with. We have found some forex brokers even let you continue to use the account for longer than the time frame that they say it is good for. However, other brokers will discontinue the account as soon as the time period is up.


Forex brokers offer forex practice accounts to people as a way to get other people interested in their forex trading platform and use their forex broker services. As a result - they will collect some basic information from you when you setup your forex practice account. Depending on the broker, they may call you and see how you are doing in your demo account and see if they can help you get started in a live account. Remember they get paid a commission only when you are making trades in a live forex account not the forex practice account.

Our advice is to use a forex practice account until you have tested your forex trading strategy and are comfortable trading the forex market. There is nothing worse than making a mistake in a live account, especially when its something that you should have learned not to do in your practice account. If you aren’t sure yet of how you are going to trade the forex market and you are looking for a simple and easy to use system that will take about 15 minutes to use - you should check out Freedom Rocks - it is an effective and easy to use forex trading system.

We have learned a lot using forex practice accounts to test out different strategies and test new theories. Often times we will be running anywhere from 3 to 5 practice accounts at the same time just to try out different forex strategies. Some brokers make it easy to have several accounts and other brokers make it difficult. The broker we use allows us to create new demo accounts in just a few mouse clicks and they don’t care how many accounts you have – as a result – it makes it a lot easier to test strategies on their platform as opposed to other forex brokers.

Even after you have been trading the forex market for a long time you will want to experiment and test out new methods of trading and that is what forex practice accounts are great for.
Test your new forex method without putting any money at risk in a demo account

Fundamental Factors

In economics, inflation is a rise in the general level of prices of goods and services in an economy over a period of time. The term "inflation" once referred to increases in the money supply (monetary inflation); however, economic debates about the relationship between money supply and price levels have led to its primary use today in describing price inflation. Inflation can also be described as a decline in the real value of money—a loss of purchasing power in the medium of exchange which is also the monetary unit of account. When the general price level rises, each unit of currency buys fewer goods and services. A chief measure of price inflation is the inflation rate, which is the percentage change in a price index over time.
Inflation can cause adverse effects on the economy. For example, uncertainty about future inflation may discourage investment and saving. High inflation may lead to shortages of goods if consumers begin hoarding out of concern that prices will increase in the future.
Economists generally agree that high rates of inflation and hyperinflation are caused by an excessive growth of the money supply. Views on which factors determine low to moderate rates of inflation are more varied. Low or moderate inflation may be attributed to fluctuations in real demand for goods and services, or changes in available supplies such as during scarcities, as well as to growth in the money supply. However, the consensus view is that a long sustained period of inflation is caused by money supply growing faster than the rate of economic growth.
Today, most economists favor a low steady rate of inflation. Low (as opposed to zero or negative) inflation may reduce the severity of economic recessions by enabling the labor market to adjust more quickly in a downturn, and reducing the risk that a liquidity trap prevents monetary policy from stabilizing the economy. The task of keeping the rate of inflation low and stable is usually given to monetary authorities. Generally, these monetary authorities are the central banks that control the size of the money supply through the setting of interest rates, through open market operations, and through the setting of banking reserve requirements.

Interest Rate

An interest rate is the price a borrower pays for the use of money they do not own, for instance a small company might borrow from a bank to kick start their business, and the return a lender receives for deferring the use of funds, by lending it to the borrower. Interest rates are normally expressed as a percentage rate over the period of one year.
Interest rates targets are also a vital tool of monetary policy and are used to control variables like investment, inflation, and unemployment.

Unemployment Rate

Unemployment occurs when a person is available to work and currently seeking work, but the person is without work. The prevalence of unemployment is usually measured using the unemployment rate, which is defined as the percentage of those in the labor force who are unemployed. The unemployment rate is also used in economic studies and economic indexes such as the United States' Conference Board's Index of Leading Indicators as a measure of the state of the macroeconomics.
There are a variety of different causes of unemployment, and disagreement on which causes are most important. Different schools of economic thought suggest different policies to address unemployment. Monetarists for example, believe that controlling inflation to facilitate growth and investment is more important, and will lead to increased employment in the long run. Keynesians on the other hand emphasize the smoothing out of business cycles by manipulating aggregate demand. There is also disagreement on how exactly to measure unemployment. Different countries experience different levels of unemployment; the USA currently experiences lower unemployment levels than the European Union, and it also changes over time (e.g. the Great depression) throughout economic cycles.

Monetary Policy

Monetary policy is the process by which the government, central bank, or monetary authority of a country controls the supply of money, availability of money, and cost of money or rate of interest, in order to attain a set of objectives oriented towards the growth and stability of the economy. Monetary theory provides insight into how to craft optimal monetary policy.
Monetary policy is referred to as either being an expansionary policy, or a contractionary policy, where an expansionary policy increases the total supply of money in the economy, and a contractionary policy decreases the total money supply. Expansionary policy is traditionally used to combat unemployment in a recession by lowering interest rates, while contractionary policy involves raising interest rates in order to combat inflation. Monetary policy should be contrasted with fiscal policy, which refers to government borrowing, spending and taxation.

There are many more fundamental factors but the most important are those which I described in this article.
Thanks to Wikipedia

Support & Resistance

Support and resistance is a concept in technical analysis that the movement of the price of a security will tend to stop and reverse at certain predetermined price levels.

Support

A support level is a price level where the price tends to find support as it is going down. This means the price is more likely to "bounce" off this level rather than break through it. However, once the price has passed this level, by an amount exceeding some noise, it is likely to continue dropping until it finds another support level.

Resistance

A resistance level is the opposite of a support level. It is where the price tends to find resistance as it is going up. This means the price is more likely to "bounce" off this level rather than break through it. However, once the price has passed this level, by an amount exceeding some noise, it is likely that it will continue rising until it finds another resistance level.

Identifying support and resistance levels

Support and resistance levels can be identified by trend lines. Some traders believe in using pivot point calculations.

The more often a support/resistance level is "tested" (touched and bounced off by price), the more significance given to that specific level.

If a price breaks past a support level, that support level often becomes a new resistance level. The opposite is true as well, if price breaks a resistance level, it will often find support at that level in the future.

Using support and resistance levels

This is an example of support switching roles with resistance, and vice versa:

If a stock price is moving between support and resistance levels, then a basic investment strategy commonly used by traders, is to buy a stock at support and sell at resistance, then short at resistance and cover the short at support as per the following example:

When judging entry and exit investment timing using support or resistance levels it is important to choose a chart based on a price interval period that aligns with your trading strategy timeframe. Short term traders tend to use charts based on interval periods, such as 1 minute (i.e. the price of the security is plotted on the chart every 1 minute), with longer term traders using price charts based on hourly, daily, weekly or monthly interval periods. Typically traders use shorter term interval charts when making a final decisions on when to invest, such as the following example based on 1 week of historical data with price plotted every 15 minutes. In this example the early signs that the stock was coming out of a downtrend was when it started to form support at $30.48 and then started to form higher highs and higher lows signalling a change from negative to positive trending.

Pivot Points

Pivot point calculations are used by security traders to attempt to predict support and resistance levels. They are commonly used in the forex and commodity futures markets. Pivot point analysis is a subset of technical analysis, although it is not as popular or well known as other technical methods.

Pivot points are frequently used by foreign exchange traders as a means to calculate resistance and support levels which are, in turn, used as visual cues to execute trades. Pivot point calculations provide traders with objective visual bench marks which some use to predict price changes, although the validity of these levels is still subject to debate.

Calculating points

The popular pivot points include PP, and various "support" and "resistance" points based on PP. There are various ways to calculate PP, including the averaging of the High, Low, and Close (H, L, C) of the previous day's trading, or including the Open trading price in the average. Some popular examples follow:

* R4 = PP + RANGE*3
* R3 = PP + RANGE*2
* R2 = PP + RANGE
* R1 = 2 * PP - L
* PP = (H+L+C) / 3
* S1 = 2 * PP - H
* S2: PP - RANGE
* S3: PP - RANGE*2
* S4: PP - RANGE*3

Accredited Investors Law

Under the Securities Act of 1933, a company that offers or sells its securities must register the securities with the SEC or find an exemption from the registration requirements. The Act provides companies with a number of exemptions. For some of the exemptions, such as rules 505 and 506 of Regulation D, a company may sell its securities to what are known as "accredited investors."

The federal securities laws define the term accredited investor in Rule 501 of Regulation D as:

1. a bank, insurance company, registered investment company, business development company, or small business investment company;

2. an employee benefit plan, within the meaning of the Employee Retirement Income Security Act, if a bank, insurance company, or registered investment adviser makes the investment decisions, or if the plan has total assets in excess of $5 million;

3. a charitable organization, corporation, or partnership with assets exceeding $5 million;

4. a director, executive officer, or general partner of the company selling the securities;

5. a business in which all the equity owners are accredited investors;

6. a natural person who has individual net worth, or joint net worth with the person’s spouse, that exceeds $1 million at the time of the purchase;

7. a natural person with income exceeding $200,000 in each of the two most recent years or joint income with a spouse exceeding $300,000 for those years and a reasonable expectation of the same income level in the current year; or

8. a trust with assets in excess of $5 million, not formed to acquire the securities offered, whose purchases a sophisticated person makes.

For more information about the SEC’s registration requirements and common exemptions, read our brochure, Q&A: Small Business & the SEC.

http://www.sec.gov/answers/accred.htm

Disclaimer: This document copied from U.S. Securities and Exchange Commission and the link is http://www.sec.gov/answers/accred.htm.

Forex

Buying/Selling

In the forex market currencies are always priced in pairs; therefore all trades result in the simultaneous buying of one currency and the selling of another. The objective of currency trading is to buy the currency that increases in value relative to the one you sold. If you have bought a currency and the price appreciates in value, then you must sell the currency back in order to lock in the profit.

Quoting Conventions

Currencies are quoted in pairs. The first listed currency is known as the base currency, while the second is called the counter or quote currency. In the wholesale market, currencies are quoted using five significant numbers, with the last placeholder called a point or a pip.

Like all financial products, FX quotes include a "bid" and "ask". By quoting both the bid and ask in real time, GFM ensures that traders always receive a fair price on all transactions. As in any traded instrument, there is an immediate cost in establishing a position. For example, USD/JPY may bid at 131.40 and ask at 131.45, this five-pip spread defines the trader’s cost, which can be recovered with a favorable currency move in the market.

Margin

The margin deposit is not a down payment on a purchase of equity, as many perceive margins to be in the stock markets. Rather, the margin is a performance bond, or good faith deposit, to ensure against trading losses. The margin requirement allows traders to hold a position much larger than the account value. GFM’ s online trading platform has margin management capabilities, which allow for this high leverage.

In the event that funds in the account fall below margin requirements, the GFM Dealing Desk will close all open positions. This prevents clients' accounts from falling into a negative balance, even in a highly volatile, fast moving market.

Rollover

For positions open at 5pm EST, there is a daily rollover interest rate a trader either pays or earns, depending on your established margin and position in the market. If you do not want to earn or pay interest on your positions, simply make sure it is closed at 5pm EST, the established end of the market day.

What Every Currency Trader Should Know

The forex market is one of the most popular markets for speculation due to its enormous size, liquidity, and tendency for currencies to move in strong trends. An enticing aspect of trading currencies is the high degree of leverage available. GFM allows positions to be leveraged up to 100:1. Without proper risk management, this high degree of leverage can lead to enormous swings between profit and loss. Knowing that even seasoned traders suffer losses, speculation in the forex market should only be conducted with risk capital funds that if lost will not significantly affect one's personal financial well being.

The GFM Mini account was designed for those new to online currency trading. There is a smaller deposit required to open an GFM Mini account and trading sizes are 1/10th the size of a regular account. The smaller trade size enables traders to take smaller risks. The GFM Mini is intended to introduce traders to the excitement of currency trading while minimizing risk.

Buying/Selling

In the forex market currencies are always priced in pairs; therefore all trades result in the simultaneous buying of one currency and the selling of another. The objective of currency trading is to buy the currency that increases in value relative to the one you sold. If you have bought a currency and the price appreciates in value, then you must sell the currency back in order to lock in the profit.

Quoting Conventions

Currencies are quoted in pairs. The first listed currency is known as the base currency, while the second is called the counter or quote currency. In the wholesale market, currencies are quoted using five significant numbers, with the last placeholder called a point or a pip.

Like all financial products, FX quotes include a "bid" and "ask". By quoting both the bid and ask in real time, GFM ensures that traders always receive a fair price on all transactions. As in any traded instrument, there is an immediate cost in establishing a position. For example, USD/JPY may bid at 131.40 and ask at 131.45, this five-pip spread defines the trader’s cost, which can be recovered with a favorable currency move in the market.

Margin

The margin deposit is not a down payment on a purchase of equity, as many perceive margins to be in the stock markets. Rather, the margin is a performance bond, or good faith deposit, to ensure against trading losses. The margin requirement allows traders to hold a position much larger than the account value. GFM’ s online trading platform has margin management capabilities, which allow for this high leverage.

In the event that funds in the account fall below margin requirements, the GFM Dealing Desk will close all open positions. This prevents clients' accounts from falling into a negative balance, even in a highly volatile, fast moving market.

Rollover

For positions open at 5pm EST, there is a daily rollover interest rate a trader either pays or earns, depending on your established margin and position in the market. If you do not want to earn or pay interest on your positions, simply make sure it is closed at 5pm EST, the established end of the market day.

What Every Currency Trader Should Know

The forex market is one of the most popular markets for speculation due to its enormous size, liquidity, and tendency for currencies to move in strong trends. An enticing aspect of trading currencies is the high degree of leverage available. GFM allows positions to be leveraged up to 100:1. Without proper risk management, this high degree of leverage can lead to enormous swings between profit and loss. Knowing that even seasoned traders suffer losses, speculation in the forex market should only be conducted with risk capital funds that if lost will not significantly affect one's personal financial well being.

The GFM Mini account was designed for those new to online currency trading. There is a smaller deposit required to open an GFM Mini account and trading sizes are 1/10th the size of a regular account. The smaller trade size enables traders to take smaller risks. The GFM Mini is intended to introduce traders to the excitement of currency trading while minimizing risk.

Bollinger bands

Bollinger Bands are a technical analysis tool invented by John Bollinger in the 1980s. Having evolved from the concept of trading bands, Bollinger Bands can be used to measure the highness or lowness of the price relative to previous trades.
Bollinger Bands consist of:
a middle band being an N-period simple moving average
an upper band at K times an N-period standard deviation above the middle band
a lower band at K times an N-period standard deviation below the middle band
Typical values for N and K are 20 and 2, respectively. The default choice for the average is a simple moving average, but other types of averages can be employed as needed. Exponential moving averages are a common second choice. Usually the same period is used for both the middle band and the calculation of standard deviation.

Indicators Derived from Bollinger Bands

There are two indicators derived from Bollinger Bands, %b and BandWidth.
%b, pronounced 'percent b', is derived from the formula for Stochastics and tells you where you are in relation to the bands. %b equals 1 at the upper band and 0 at the lower band.
%b = (last - lowerBB) / (upperBB - lowerBB)
BandWidth tells you how wide the Bollinger Bands are on a normalized basis.
BandWidth = (upperBB - lowerBB) / middleBB
Using the default parameters of a 20-period look back and plus/minus two standard deviations, BandWidth is equal to four times the 20-period coefficient of variation.
Uses for %b include system building and pattern recognition. Uses for BandWidth include identification of opportunities arising from relative extremes in volatility and trend identification.

Purpose

The purpose of Bollinger Bands is to provide a relative definition of high and low. By definition, prices are high at the upper band and low at the lower band. This definition can aid in rigorous pattern recognition and is useful in comparing price action to the action of indicators to arrive at systematic trading decisions.

Interpretation

The use of Bollinger Bands varies wildly among traders. Some traders buy when price touches the lower Bollinger Band and exit when price touches the moving average in the center of the bands. Other traders buy when price breaks above the upper Bollinger Band or sell when price falls below the lower Bollinger Band. Moreover, the use of Bollinger Bands is not confined to stock traders; options traders, most notably implied volatility traders, often sell options when Bollinger Bands are historically far apart or buy options when the Bollinger Bands are historically close together, in both instances, expecting volatility to revert back towards the average historical volatility level for the stock.-technical analysis.
When the bands lie close together a period of low volatility in stock price is indicated. When they are far apart a period of high volatility in price is indicated. When the bands have only a slight slope and lie approximately parallel for an extended time the price of a stock will be found to oscillate up and down between the bands as though in a channel.
As always, traders are inclined to use Bollinger Bands with other indicators to see if there is confirmation. In particular, the use of an oscillator like Bollinger Bands will often be coupled with a non-oscillator indicator like chart patterns or a trendline; if these indicators confirm the recommendation of the Bollinger Bands, the trader will have greater evidence that what the Bands forecast is correct.

Effectiveness

Bollinger Band trading strategies may be effective in the Chinese marketplace as concluded by a recent study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the moving average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50 percent." Nauzer J. Balsara, Gary Chen and Lin Zheng The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rules [2]

Statistical Properties

Security prices have no known statistical distribution, Normal or otherwise; they are known to have fat tails, compared to the Normal.[3] The sample size typically used, 20, is too small for conclusions derived from statistical techniques like the Central Limit Theorem to be reliable. Such techniques usually require the sample to be independent and identically distributed which is not the case for a time series like security prices.
For these three primary reasons, it is incorrect to assume that the percentage of the data outside the Bollinger Bands will always be limited to a certain amount. So, instead of finding about 95% of the data inside the bands, as would be the expectation with the default parameters if the data were normally distributed, one will typically find less; how much less is a function of the security's volatility.

Moving Average Convergence / Divergence (MACD)

MACD, which stands for Moving Average Convergence / Divergence, is a technical analysis indicator created by Gerald Appel in the 1960s. It shows the difference between a fast and slow exponential moving average (EMA) of closing prices. During the 1980s MACD proved to be a valuable tool for any trader. The standard periods recommended back in the 1960s by Gerald Appel are 12 and 26 days:

A signal line (or trigger line) is then formed by smoothing this with a further EMA. The standard period for this is 9 days,

The difference between the MACD and the signal line is often calculated and shown not as a line, but a solid block histogram style. This construction was made by Thomas Aspray in 1986. The calculation is simply
histogram = MACD − signal
The example graph above right shows all three of these together. The upper graph is the prices. The lower graph has the MACD line in blue and the signal line in red. The solid white histogram style is the difference between them.
The set of periods for the averages, often written as say 12,26,9, can be varied. Appel and others have experimented with various combinations.

Interpretation

MACD is a trend following indicator, and is designed to identify trend changes. It's generally not recommended for use in ranging market conditions. Three types of trading signals are generated:
MACD line crossing the signal line.
MACD line crossing 0.
Divergence between price and histogram, or between MACD line and price.
The signal line crossing is the usual trading rule. This is to buy when the MACD crosses up through the signal line, or sell when it crosses down through the signal line. These crossings may occur too frequently, and other tests may have to be applied.
The histogram shows when a crossing occurs. When the MACD line crosses through zero on the histogram it is said that the MACD line has crossed the signal line. The histogram can also help visualizing when the two lines are coming together. Both may still be rising, but coming together, so a falling histogram suggests a crossover may be approaching.
A crossing of the MACD line up through zero is interpreted as bullish, or down through zero as bearish. These crossings are of course simply the original EMA(12) line crossing up or down through the slower EMA(26) line.
Positive divergence between MACD and price arises when price makes a new selloff low, but the MACD doesn't make a new low (i.e. it remains above where it fell to on that previous price low). This is interpreted as bullish, suggesting the downtrend may be nearly over. Negative divergence is the same thing when rising (i.e. price makes a new rally high, but MACD doesn't rise as high as before), this is interpreted as bearish.
Divergence may be similarly interpreted on the price versus the histogram, where the new price levels are not confirmed by new histogram levels. Longer and sharper divergences (distinct peaks or troughs) are regarded as more significant than small shallow patterns in this case.
It is recommended to look at a MACD on a weekly scale before looking at a daily scale to avoid making short term trades against the direction of the intermediate trend.
Sometimes it is prudent to apply a price filter to the Bullish Moving Average Crossover to ensure that it will hold. An example of a price filter would be to buy if MACD breaks above the 9-day EMA and remains above for three days. The buy signal would then commence at the end of the third day.

Creator

Gerald Appel developed the Moving Average Convergence-Divergence (MACD) Indicator, and has written twelve books relating to investment strategies, including Winning Market Systems, Double Your Money Every Three Years, Stock Market Trading Systems, The Big Move, New Directions in Technical Analysis (co-author, Dr. Martin E. Zweig), and Time-Trend III. In addition, Gerald has had many articles published in such publications as Money, Barron's, and Stocks and Commodities magazines. He has also produced and appeared in a number of videotapes related to technical investment strategies. Gerald has presented at a number of seminars within the United States and abroad, and has appeared on "Wall Street Week" with Louis Rukeyser and is a frequent guest on other television programs on the financial news networks and elsewhere.
Mr Appel published frequently in the 1970s & 1980s, in a time when computerized tools became available to professional investors but were much less accessible to retail investors. As computerized technical analysis relies on back-testing, it is not surprising that strategies announced year-by-year by Mr. Appel became vulnerable, year-by-year, to failure, as "past results do not guarantee future success". Nevertheless, the author's research has been respectable & innovative.

Signal Processing

The MACD is a filtered measure of the velocity. The velocity has been passed through two first order linear low pass filters. The "signal line" is that resulting velocity, filtered again. The difference between those two, the histogram, is a measure of the acceleration, with all three filters applied. The "MACD crossing the signal line" suggests that the direction of the acceleration is changing. "MACD line crossing zero" suggests that the average velocity is changing direction.

Criticism

With the emergence of computerized analysis, it has become highly unreliable in the modern era, and standard MACD based trade execution now produces a greater distribution of losing trades[citation needed]. Some additions have been made to MACD over the years but even with the addition of the MACD histogram, it remains a lagging indicator. It has often been criticized for failing to respond in mild/volatile market conditions. Since the crash of the market in 2000, most strategies no longer recommend using MACD as the primary method of analysis, but instead believe it should be used as a monitoring tool only. It is prone to whipsaw, and if a trader is not careful it is possible that they might suffer substantial loss, especially if they are leveraged or trading options.

Relative Strength Index

The Relative Strength Index (RSI) is a financial technical analysis oscillator showing price strength by comparing upward and downward close-to-close movements.
The RSI is popular because it is relatively easy to interpret. It was developed by J. Welles Wilder and published in Commodities magazine (now called Futures magazine) in June 1978, and in his New Concepts in Technical Trading Systems the same year.
Note that the term relative strength also refers to the strength of a security in relation to its sector or the overall market. For instance, XYZ might rise 2% when S&P 500 rises 1%. This is sometimes called comparative relative strength to avoid confusion. It's unrelated to the Relative Strength Index described here.

Calculation

For each day an upward change (U) or downward change (D) is calculated. "Up" days are characterized by the daily close being higher than yesterday's daily close, i.e.:
U = closetoday − closeyesterday
D = 0
Conversely, a down day is characterized by the close being lower than the previous day's (note that D is nonetheless a positive number),
U = 0
D = closeyesterday − closetoday
If today's close is the same as yesterday's, both U and D are zero. An average for U is calculated with an exponential moving average using a given N-days smoothing factor, and likewise for D. The ratio of those averages is the Relative Strength,

This is converted to a Relative Strength Index between 0 and 100,

This can be rewritten as follows to emphasise the way RSI expresses the up as a proportion of the total up and down (averages in each case),

The EMA in theory uses an infinite amount of past data (as discussed in the EMA article). It's necessary either to go back far enough, or alternately at the start of data begin with a simple average of N days instead,

and then continue from there with the usual EMA formula,

(Similarly with D.)

Interpretation


Wilder recommended a smoothing period of 14. This is by his reckoning of EMA smoothing (see the EMA article on that), ie. α=1/14 or N=27.
Wilder considered a security overbought if it reached the 70 level, meaning that the speculator should consider selling. Or conversely oversold at the 30 level. The principle is that when there's a high proportion of daily movement in one direction it suggests an extreme, and prices are likely to reverse. Levels 80 and 20 are also used, or may be varied according to market conditions (eg. a bull market may have an upward bias).
Large surges and drops in securities will affect RSI, but it could just be a false buy or sell. The RSI is best used as a complement with other technical analysis indicators.
The RSI should confirm price movement; therefore, if the stock price is moving up, the RSI should be moving up as well.

Cutler's RSI

A variation called Cutler's RSI is based on a simple moving average of U and D,[5] instead of the exponential average above.

This is like the initial data point calculation shown above, but used on every day, not just the first. The divisor N in the SMAs in the numerator and denominator cancel out, so one needn't do those divisions, instead just a sum of U and a sum of D over the past N days can be made.
Cutler's RSI generally comes out slightly different from the normal Wilder RSI, but the two are similar, since SMA and EMA are similar.

Moving Average

In statistics, a moving average, also called a rolling average and sometimes a running average, is used to analyze a set of data points by creating a series of averages of different subsets of the full data set. So a moving average is not a single number, but it is a set of numbers, each of which is the average of the corresponding subset of a larger set of data points. A simple example is if you had a data set with 100 data points, the first value of the moving average might be the arithmetic mean (one simple type of average) of data points 1 through 25. The next value would be this simple average of data points 2 through 26, and so forth, until the final value, which would be the same simple average of data points 76 through 100.
The size of the subset being averaged is often constant, as in the previous example, but need not be. In particular, a cumulative average is a type of moving average where each value is the average of all previous data points in the full data set. So the size of the subset being averaged grows by one as each new value of the moving average is calculated. A moving average could also use a weighted average instead of a simple average, perhaps in order to place more emphasis on more recent data points than on other data points longer ago in time.
A moving average can be applied to any data set, but is perhaps most commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. For example, it is often used in technical analysis of financial data, like stock prices, returns or trading volumes. It is also used in economics to examine gross domestic product, employment or other macroeconomic time series. Mathematically, a moving average is a type of convolution and so it is also similar to the low-pass filter used in signal processing. When used with non-time series data, a moving average simply acts as a generic smoothing operation without any specific connection to time, although typically some kind of ordering is implied.

Simple moving average

Prior moving average
A simple moving average (SMA) is the unweighted mean of the previous n data points. For example, a 10-day simple moving average of closing price is the mean of the previous 10 days' closing prices. If those prices are then the formula is

When calculating successive values, a new value comes into the sum and an old value drops out, meaning a full summation each time is unnecessary,

In technical analysis there are various popular values for n, like 10 days, 40 days, or 200 days. The period selected depends on the kind of movement one is concentrating on, such as short, intermediate, or long term. In any case moving average levels are interpreted as support in a rising market, or resistance in a falling market.
In all cases a moving average lags behind the latest data point, simply from the nature of its smoothing. An SMA can lag to an undesirable extent, and can be disproportionately influenced by old data points dropping out of the average. This is addressed by giving extra weight to more recent data points, as in the weighted and exponential moving averages.
One characteristic of the SMA is that if the data have a periodic fluctuation, then applying an SMA of that period will eliminate that variation (the average always containing one complete cycle). But a perfectly regular cycle is rarely encountered in economics or finance.
Central moving average
For a number of applications it is advantageous to avoid the shifting induced by using only 'past' data. Hence a central moving average can be computed, using both 'past' and 'future' data. The 'future' data in this case are not predictions, but merely data obtained after the time at which the average is to be computed.
Weighted and exponential moving averages[citation needed] (see below) can also be computed centrally.

Cumulative moving average

The cumulative moving average is also frequently called a running average or a long running average although the term running average is also used as synonym for a moving average. This article uses the term cumulative moving average or simply cumulative average since this term is more descriptive and unambiguous.
In some data acquisition systems, the data arrives in an ordered data stream and the statistician would like to get the average of all of the data up until the current data point. For example, an investor may want the average price of all of the stock transactions for a particular stock up until the current time. As each new transaction occurs, the average price at the time of the transaction can be calculated for all of the transactions up to that point using the cumulative average. This is the cumulative average, which is typically an unweighted average of the sequence of i values x1, ..., xi up to the current time:

The brute force method to calculate this would be to store all of the data and calculate the sum and divide by the number of data points every time a new data point arrived. However, it is possible to simply update cumulative average as a new value xi+1 becomes available, using the formula:

where CA0 can be taken to be equal to 0.
Thus the current cumulative average for a new data point is equal to the previous cumulative average plus the difference between the latest data point and the previous average divided by the number of points received so far. When all of the data points arrive (i = N), the cumulative average will equal the final average.
The derivation of the cumulative average formula is straightforward. Using

and similarly for i + 1, it is seen that

Solving this equation for CAi+1 results in:

Weighted moving average

A weighted average is any average that has multiplying factors to give different weights to different data points. Mathematically, the moving average is the convolution of the data points with a moving average function; in technical analysis, a weighted moving average (WMA) has the specific meaning of weights which decrease arithmetically. In an n-day WMA the latest day has weight n, the second latest n − 1, etc, down to zero.



WMA weights n = 15
The denominator is a triangle number, and can be easily computed as
When calculating the WMA across successive values, it can be noted the difference between the numerators of WMAM+1 and WMAM is npM+1 − pM − ... − pM−n+1. If we denote the sum pM + ... + pM−n+1 by TotalM, then



The graph at the right shows how the weights decrease, from highest weight for the most recent data points, down to zero. It can be compared to the weights in the exponential moving average which follows.


Exponential moving average


EMA weights N=15
An exponential moving average (EMA), sometimes also called an exponentially weighted moving average (EWMA), applies weighting factors which decrease exponentially. The weighting for each older data point decreases exponentially, giving much more importance to recent observations while still not discarding older observations entirely. The graph at right shows an example of the weight decrease.
The degree of weighing decrease is expressed as a constant smoothing factor α, a number between 0 and 1. α may be expressed as a percentage, so a smoothing factor of 10% is equivalent to α = 0.1. Alternatively, α may be expressed in terms of N time periods, where . For example, N=19 is equivalent to α = 0.1. The half-life of the weights (the interval over which the weights decrease by a factor of two) is approximately (within 1% if N>5).
The observation at a time period t is designated Yt, and the value of the EMA at any time period t is designated St. S1 is undefined. S2 may be initialized in a number of different ways, most commonly by setting S2 to Y1, though other techniques exist, such as setting S2 to an average of the first 4 or 5 observations. The prominence of the S2 initialization's effect on the resultant moving average depends on α; smaller α values make the choice of S2 relatively more important than larger α values, since a higher α discounts older observations faster.
The formula for calculating the EMA at time periods t > 2 is

This formulation is according to Hunter (1986)[2]. The weights will obey α(1 − α)xYt − (x + 1). An alternate approach by Roberts (1959) uses Yt in lieu of Yt−1:

This formula can also be expressed in technical analysis terms as follows, showing how the EMA steps towards the latest data point, but only by a proportion of the difference (each time):

Expanding out EMA yesterday each time results in the following power series, showing how the weighting factor on each data point p1, p2, etc, decrease exponentially:

In theory this is an infinite sum, but because 1 − α is less than 1, the terms become smaller and smaller, and can be ignored once small enough. The denominator approaches 1/α, and that value can be used instead of adding up the powers, provided one is using enough terms that the omitted portion is negligible.
The N periods in an N-day EMA only specify the α factor. N is not a stopping point for the calculation in the way it is in an SMA or WMA. The first N data points in an EMA represent about 86% of the total weight in the calculation[citation needed].
The power formula above gives a starting value for a particular day, after which the successive days formula shown first can be applied.
The question of how far back to go for an initial value depends, in the worst case, on the data. If there are huge p price values in old data then they'll have an effect on the total even if their weighting is very small. If one assumes prices don't vary too wildly then just the weighting can be considered. The weight omitted by stopping after k terms is

which is

i.e. a fraction

out of the total weight.
For example, to have 99.9% of the weight,

terms should be used. Since approaches
as N increases, this simplifies to approximately
k = 3.45(N + 1)
for this example (99.9% weight).

Chart Indicators

Most trading charts come with a vast array of indicators to help make trading decisions, moving average, RSI and MACD are some of the more commonly used ones. These indicators give the user (or "trader") a visual aid to determine whether a market is changing direction or is showing signs of movement from which they could profit from.

Charting terms and Indicators

Types of Charts
Candle Stick:
A candlestick chart is a style of bar-chart used primarily to describe price movements of an equity over time.
It is a combination of a line-chart and a bar-chart, in that each bar represents the range of price movement over a given time interval. It is most often used in technical analysis of equity and currency price patterns. They appear superficially similar to error bars, but are unrelated.

History

Candlestick charts are said to have been developed in the 18th century by legendary Japanese rice trader Homma Munehisa. The charts gave Homma and others an overview of open, high, low, and close market prices over a certain period. This style of charting is very popular due to the level of ease in reading and understanding the graphs. Since the 17th century, there has been a lot of effort to relate chart patterns to the likely future behavior of a market. This method of charting prices proved to be particularly interesting, due to the ability to display five data points instead of one. The Japanese rice traders also found that the resulting charts would provide a fairly reliable tool to predict future demand.
The method was picked up by Charles Dow around 1900 and remains in common use by today's traders of financial instruments.
Candlestick chart topics

The basic candlestick
Candlestick layout
Candlesticks are usually composed of the body (black or white), an upper and a lower shadow (wick). The wick illustrates the highest and lowest traded prices of a stock during the time interval represented. The body illustrates the opening and closing trades. If the stock closed higher than it opened, the body is white, with the opening price at the bottom of the body and the closing price at the top. If the stock closed lower than it opened, the body is black, with the opening price at the top and the closing price at the bottom. A candlestick need not have either a body or a wick.
Candlestick simple patterns

There are multiple forms of candlestick chart patterns, with the simplest depicted at right. Here is a quick overview of their names:
White candlestick - signals uptrend movement (those occur in different lengths; the longer the body, the more significant the price increase)
Black candlestick - signals downtrend movement (those occur in different lengths; the longer the body, the more significant the price decrease)
Long lower shadow - bullish signal (the lower wick must be at least the body's size; the longer the lower wick, the more reliable the signal)
Long upper shadow - bearish signal (the upper wick must be at least the body's size; the longer the upper wick, the more reliable the signal)
Hammer - a bullish pattern during a downtrend (long lower wick and small or no body); Shaven head - a bullish pattern during a downtrend & a bearish pattern during an uptrend (no upper wick); Hanging man - bearish pattern during an uptrend (long lower wick, small or no body; wick has the multiple length of the body.
Inverted hammer - signals bottom reversal, however confirmation must be obtained from next trade (may be either a white or black body); Shaven bottom - signaling bottom reversal, however confirmation must be obtained from next trade (no lower wick); Shooting star - a bearish pattern during an uptrend (small body, long upper wick, small or no lower wick)
Spinning top white - neutral pattern, meaningful in combination with other candlestick patterns
Spinning top black - neutral pattern, meaningful in combination with other candlestick patterns
Doji - neutral pattern, meaningful in combination with other candlestick patterns
Long legged doji - signals a top reversal
Dragonfly doji - signals trend reversal (no upper wick, long lower wick)
Gravestone doji - signals trend reversal (no lower wick, long upper wick)
Marubozu white - dominant bullish trades, continued bullish trend (no upper, no lower wick)
Marubozu black - dominant bearish trades, continued bearish trend (no upper, no lower wick)
Complex Patterns


Itimoku Kinkô Hyô, the applied candlestick chart commonly used in Japan.
Despite those rather simple patterns depicted in the section above, there are more complex and difficult patterns, which have been identified since the charting method's inception.
Candlestick charts also convey more information than other forms of charts, such as bar charts. Just as with bar charts, they display the absolute values of the open, high, low, and closing price for a given period. But, they also show how those prices are relative to the prior periods' prices, so one can tell by looking at one bar if the price action is higher or lower than the prior one. That and they are visually easier to look at, and can be colorized for even better definition.
Use of candlestick charts
Candlestick charts are a visual aid for decision making in stock, forex, commodity, and options trading. For example, when the bar is white and high relative to other time periods, it means buyers are very bullish. The opposite is true for a black bar.

Line Chart

A line chart or line graph is a type of graph created by connecting a series of data points together with a line.[1]

Overview

A line chart is a basic type of chart common in many fields. It is an extension of a scatter graph, and is created by connecting a series of points that represent individual measurements with line segments. A line chart is often used to visualize a trend in data over intervals of time, thus the line is often drawn chronologically.
Example

In the experimental sciences, data collected from experiments are often visualized by a graph that includes an overlaid mathematical function depicting the best-fit trend of the scattered data. This layer is referred to as a best-fit layer and the graph containing this layer is often referred to as a line graph.
For example, if one were to collect data on the speed of a body at certain points in time, one could visualize the data by a data table such as the following:


Graph of Speed Vs Time
Elapsed Time (s) Speed (ms-1)
0 0
1 3
2 7
3 12
4 20
5 30
6 45
The table "visualization" is a good way of displaying precise values, but a very poor way of understanding the underlying patterns that those values represent. Because of these qualities, the table display is often erroneously conflated with the data itself; whereas it is just another visualization of the data.
Understanding the process described by the data in the table is aided by producing a graph or line chart of Speed versus Time. In this context, Versus (or the abbreviations vs and VS), separates the parameters appearing in an X-Y (two-dimensional) graph. The first argument indicates the dependent variable, usually appearing on the Y-axis, while the second argument indicates the independent variable, usually appearing on the X-axis. Thus the graph of Speed versus Time would plot time along the x-axis and speed up the y-axis. Mathematically, if we denote time by the variable t, and speed by v, then the function plotted in the graph would be denoted v(t) indicating that v (the dependent variable) is a function of t.
It is simple to construct a "best-fit" layer consisting of a set of line segments connecting adjacent data points; however, such a "best-fit" is usually not an ideal representation of the trend of the underlying scatter data for the following reasons:
It is highly improbable that the discontinuities in the slope of the best-fit would correspond exactly with the positions of the measurement values.
It is highly unlikely that the experimental error in the data is negligible, yet the curve falls exactly through each of the data points.
A true best-fit layer should depict a continuous mathematical function whose parameters are determined by using a suitable error-minimization scheme, which appropriately weights the error in the data values.
In either case, the best-fit layer can reveal trends in the data. Further, measurements such as the gradient or the area under the curve can be made visually, leading to more conclusions from the data.

Open-high-low-close chart (OHLC)

An open-high-low-close chart (also OHLC chart, or simply bar chart) is a type of chart typically used to illustrate movements in the price of a financial instrument over time. Each vertical line on the chart shows the price range (the highest and lowest prices) over one unit of time, e.g. one day or one hour. Tick marks project from each side of the line indicating the opening price (e.g. for a daily bar chart this would be the starting price for that day) on the left, and the closing price for that time period on the right. The bars may be shown in different hues depending on whether prices rose or fell in that period.
The Japanese candlestick chart is another way of displaying market price data, with the opening and closing prices defining a rectangle within the range for each time unit. Both charts show the exact same data, i.e. the opening, high, low, and closing prices during a particular time frame. Some traders find the candlestick chart easier to read.

Systematic Trading

Neural networks
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.
As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.
While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.
Rule-based trading
Rule-based trading is an approach intended to create trading plans using strict and clear-cut rules. Unlike some other technical methods and the approach of fundamental analysis, it defines a set of rules that determine all trades, leaving minimal discretion. The theory behind this approach is that by following a distinct set of trading rules you will reduce the number of poor decisions, which are often emotion based.
For instance, a trader might make a set of rules stating that he will take a long position whenever the price of a particular instrument closes above its 50-day moving average, and shorting it whenever it drops below.

Systematic Trading

Neural networks
Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal function approximators, meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.
As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.
While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders. However, large-scale application is problematic because of the problem of matching the correct neural topology to the market being studied.
Rule-based trading
Rule-based trading is an approach intended to create trading plans using strict and clear-cut rules. Unlike some other technical methods and the approach of fundamental analysis, it defines a set of rules that determine all trades, leaving minimal discretion. The theory behind this approach is that by following a distinct set of trading rules you will reduce the number of poor decisions, which are often emotion based.
For instance, a trader might make a set of rules stating that he will take a long position whenever the price of a particular instrument closes above its 50-day moving average, and shorting it whenever it drops below.

Use of Market Trend

Many traders say that trading in the direction of the trend is the most effective means to be profitable in financial or commodities markets. John W. Henry, Larry Hite, Ed Seykota, Richard Dennis, William Eckhardt, Victor Sperandeo, Michael Marcus and Paul Tudor Jones (some of the so-called Market Wizards in the popular book of the same name by Jack D. Schwager) have each amassed massive fortunes via the use of technical analysis and its concepts. George Lane, a technical analyst, coined one of the most popular phrases on Wall Street, "The trend is your friend!"
Many non-arbitrage algorithmic trading systems rely on the idea of trend-following, as do many hedge funds. A relatively recent trend, both in research and industrial practice, has been the development of increasingly sophisticated automated trading strategies. These often rely on underlying technical analysis principles.

Market trends

Primary Market Trends
A primary trend has broad support throughout the entire market or market sector and lasts for a year or more.
Bull market
A bull market tends to be associated with increasing investor confidence, motivating investors to buy in anticipation of future price increases and future capital gains. In describing financial market behavior, the largest group of market participants is often referred to, metaphorically, as a herd. This is especially relevant to participants in bull markets since bulls are herding animals. A bull market is also sometimes described as a bull run. Dow Theory attempts to describe the character of these market movements.
India's BSE Index SENSEX was in a bull run for almost five years from April 2003 to January 2008 as it increased from 2,900 points to 21,000 points. Another notable and recent bull market was in the 1990s when the U.S. and many other global financial markets rose rapidly.
Bear market
A bear market is a steady drop in the stock market over a period of time. It is described as being accompanied by widespread pessimism. Investors anticipating further losses are often motivated to sell, with negative sentiment feeding on itself in a vicious circle. The most famous bear market in history was preceded by the Wall Street Crash of 1929 and lasted from 1930 to 1932, marking the start of the Great Depression. A milder, low-level, long-term bear market occurred from about 1973 to 1982, encompassing the stagflation of U.S. economy, the 1970s energy crisis, and the high unemployment of the early 1980s.
Prices fluctuate constantly on the open market. To take the example of a bear stock market, it is not a simple decline, but a substantial drop in the prices of the majority of stocks over a defined period of time. According to The Vanguard Group, "While there’s no agreed-upon definition of a bear market, one generally accepted measure is a price decline of 20% or more over at least a two-month period."
Market bottom
A stock market bottom is a trend reversal - the end of a market downturn and the beginning of an upward moving trend. "Bottom" is more than just a recent low in a stock market index, but a reversal of the primary trend. A "bottom" may occur because of the presence of a "cycle," or because of "panic selling" as a reaction to an adverse financial development.
It is very difficult to identify a bottom (referred to by investors as "bottom picking") while it is occurring. The upturn following a decline is often short lived and prices might resume their decline. This would bring a loss for the investor who purchased stock(s) during a misperceived or "fake" market bottom.
Some of the more notable market bottoms, in terms of the closing values of the Dow Jones Industrial Average (DJIA) include:
Black Monday: The DJIA hit a bottom at 1738.74 on 10/19/1987, as a result of the decline from 2722.41 on 8/25/1987.
The bursting of the Dot-com bubble: A bottom of 7286.27 was reached on the DJIA on 10/9/2002 as a result of the decline from 11722.98 on 1/14/2000. This included an intermediate bottom of 8235.81 on 9/21/2001 which led to an intermediate top of 10635.25 on 3/19/2002.
A decline associated with the Subprime mortgage crisis starting at 14164.41 on 10/9/2007 (DJIA) and caused a short term bottom of 11740.15 on 3/10/2008. After a rallying to a temporary top on 5/2/2008 at 13058.20 the primary trend of the declining, "bear" market, resumed.
Baron Rothschild is said to have advised that the best time to buy is when there is "blood in the streets", i.e. when the markets have fallen drastically and investor sentiment is extremely negative.
Secondary market trends

Secondary trends are short-term changes in price direction against a primary trend. They usually last between a few weeks and a few months. Whether a trend is a secondary trend, or the beginning of a primary trend, can only be known once it has either ended or has exceeded the extent of a secondary trend.
A decline in prices during a primary trend bull market is called a market correction. A correction is usually a decline of 10% to 20%, but some experts say it can be a third or more. It differs from a bear market mostly in that it has a smaller magnitude and duration.
An increase in prices during a primary trend bear market is called a bear market rally. A bear market rally is sometimes defined as an increase of 10% to 20%. Bear market rallies typically begin suddenly and are often short-lived. Notable bear market rallies occurred in the Dow Jones index after the 1929 stock market crash leading down to the market bottom in 1932, and throughout the late 1960s and early 1970s. The Japanese Nikkei stock average has been typified by a number of bear market rallies since the late 1980s while experiencing an overall long-term downward trend.
Secular market trends

A secular market trend is a long-term trend that usually lasts 5 to 25 years (but whose distribution is more or less bell shaped around 17 years, in the stock market), and consists of sequential 'primary' trends. In a secular bull market the 'primary' bear markets have in the past almost always been shorter and less punishing than the 'primary' bull markets were rewarding. Each bear market has rarely (if ever) wiped out the real (inflation adjusted) gains of the previous bull markets, and the succeeding bull markets have usually made up for the real losses of any previous bear markets. This is one of the reasons why a secular market trend may be said to encompass the primary trends within it. The United States was described as being in a secular bull market from about 1983 to late 2007, with brief upsets including the crash of 1987 and the dot-com bust of 2000–2002.
In a secular bear market, the 'primary' bull markets are sometimes shorter than the 'primary' bear markets and rarely compensate for the real losses of the 'primary' bear markets occurring during this extended cycle. For example, in the 1966–82 secular bear market in stocks, there was hardly any nominal loss. But in real terms the loss was devastating. (In the past most 'housing recessions' were of a slow nature, thereby allowing inflation to keep housing prices steady.) Another example of a secular bear market was seen in gold during the period between January 1980 to June 1999. During this period the nominal gold price fell from a high of $850/oz ($30/g) to a low of $253/oz ($9/g), and became part of the Great Commodities Depression. The S&P 500 experienced a secular bull market over a similar time period (~1982–2000).

Market Trends

A Market trend is the direction in which a financial market is moving. Market trends can be classified as primary trends, secondary trends (short-term), and secular trends (long-term). This principle incorporates the idea that market cycles occur with regularity and persistence. This belief is considered to be generally consistent with the practice of technical analysis and broadly inconsistent with the standard academic view of financial markets, the efficient market hypothesis.Another academic viewpoint is that market prices follow a random walk model and that any apparent past 'trends' are purely an accumulation of random variations and do not serve as a predictor for future performance. Random walk theory suggests that it is therefore not possible to outperform the general market using traditional evaluations of its "fundamentals" or by using technical analysis.
However, the assumption that market prices move in trends is one of the major components of technical analysis, and consideration of market trends is common to most Wall Street investors. Market trends are described as sustained movements in market prices over a period of time. The terms bull market and bear market describe upward and downward movements respectively and can be used to describe either the market as a whole or specific sectors and securities (stocks). The expressions "bullish" and "bearish" can also mean optimistic and pessimistic respectively ("bullish on technology stocks," or "bearish on gold", etc).

History and Principles

The principles of technical analysis derive from the observation of financial markets over hundreds of years.[citation needed] The oldest known example of technical analysis was a method developed by Homma Munehisa during early 18th century which evolved into the use ofcandlestick techniques, and is today a main charting tool.[16][17]

Dow Theory is based on the collected writings of Dow Jones co-founder and editor Charles Dow, and inspired the use and development of modern technical analysis from the end of the 19th century. Other pioneers of analysis techniques include Ralph Nelson Elliott and William Delbert Gann who developed their respective techniques in the early 20th century.

Many more technical tools and theories have been developed and enhanced in recent decades, with an increasing emphasis on computer-assisted techniques.

Principles
Stock chart showing levels of support (4,5,6, 7, and 8) and resistance (1, 2, and 3); levels of resistance tend to become levels of support and vice versa.

Technicians say that a market's price reflects all relevant information, so their analysis looks more at "internals" than at "externals" such as news events. Price action also tends to repeat itself because investors collectively tend toward patterned behavior – hence technicians' focus on identifiable trends and conditions.

Technical Analysis

Technical analysis is a security analysis technique that claims the ability to forecast the future direction of prices through the study of past market data, primarily price and volume. In its purest form, technical analysis considers only the actual price and volume behavior of the market or instrument. Technical analysts, sometimes called "chartists", may employ models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, cycles or, classically, through recognition of chart patterns.
Technical analysis stands in distinction to fundamental analysis. Technical analysis "ignores" the actual nature of the company, market, currency or commodity and is based solely on "the charts," that is to say price and volume information, whereas fundamental analysis does look at the actual facts of the company, market, currency or commodity. For example, any large brokerage, trading group, or financial institution will typically have both a technical analysis and fundamental analysis team.
Technical analysis is widely used among traders and financial professionals, and is very often used by active day traders, market makers, and pit traders. In the 1960s and 1970s it was widely discredited by academic mathematics. In a recent review, Irwin and Park reported that 56 of 95 modern studies found it produces positive results, but noted that many of the positive results were rendered dubious by issues such as data snooping so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience. Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient market hypothesis. Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.
In the foreign exchange markets, its use may be more widespread than fundamental analysis. While some isolated studies have indicated that technical trading rules might lead to consistent returns in the period prior to 1987, most academic work has focused on the nature of the anomalous position of the foreign exchange market. It is speculated that this anomaly is due to central bank intervention.
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