24-hour currency trading

Foreign exchange market trading occurs over a 24 hour period picking up in Asia around 23:00 GMT (6:00 PM EST) Sunday evening and coming to an end in the United States on Friday around 22:00 GMT (5:00 PM EST). So, whether it’s 6 PM or 6 AM, somewhere in the world there are buyers and sellers actively trading foreign currencies. Traders involved in currency trading can always respond to breaking news immediately.

Although after-hours trading in stocks can be achieved via ECNs (electronic communications networks) and in futures via electronic systems like Globex, the prices can be uncompetitive since the liquidity is often low. For foreign currency trading this is not the case. The currency trader can get tight spreads around the clock and can thus pick and choose whatever trading hours are the most convenient for him.

FREE Currency Trading Training

Register online with us and get free live training over the Internet. The training is conducted by professionals. Find out more about our free currency trading training.

Little money needed to start day trading currencies

Day trading currencies requires a lot less starting capital than day trading stocks. To day trade stocks a day trader needs at least $25,000 by US law, otherwise he is restricted in the number of daily transactions he can make. This restriction does not exist in the online currency trading market. You could open an account with us with $2,500 or more and get free online training live.

No Commissions

Online discount brokers typically charge anywhere from $5 to $30 a stock trade. Full-service brokers usually charge $100 or more for each stock transaction. Futures trades can be from $10 to $30 a round turn. Forex trading with Currency Trading USA is commission free. Thus, investors involved in foreign currency trading could limit the cost associated with trading. Currency Trading USA is compensated through the Bid/Ask spread..

Lower operation fees

To be a serious stock day trader, a person needs a direct access trading system. These systems can cost from about $250 to $400 or more a month. Currency trading can be done through a sophisticated online system for free. Our Currency Trading USA trading platform is top-of-the-line and has the same (or more) features that quality stock trading systems provide. The main difference is that our currency trading system is free.

Tighter Bid/Ask Spreads

If we compare our currency trading platform’s typical spread of 3 pips on a the EUR/USD currency pair to a stock transaction, we could see how online currency trading could offer tighter spreads than stocks. A 3 pip spread (0.0003) on 1 lot (100,000 per lot) is $30. If a stock trader trades a stock with an average price of $25 a share, he would have to trade 4,000 shares to reach the 100,000 value of one currency lot. Assuming the stock is very liquid, the spread would vary between 0.01 to 0.02 or more per share throughout the day. This is equivalent to $40 to $80 per transaction, much higher than for our currency trading example.

Low Margin Requirements

Our 100:1 margin (1%) requirement for foreign currency trading allows a trader to control $100,000 worth of currency for only $1,000. This is much higher than the requirement for stocks and futures. The typical requirement for stock trading is 2:1 and 15:1 for futures trading (Increasing leverage increases risk).

The substantial leverage available in the foreign currency market is essential because the average daily move of a major currency is less than 1%. While certainly not for everyone, the substantial leverage available from online currency trading may be useful to traders that employ a disciplined trading style with strict money management principles (High Leverage and low margin can magnify or lead to both substantial profits and losses).

Superior liquidity in the currency markets

The foreign currency trading market has a daily trading volume that is larger than that of all the world stock markets put together. This means that there are always currency broker/dealers willing to buy or sell currencies in the forex markets. Consequently, price stability is assured, especially for the major the major currencies. Currency traders can almost always open or close a position at a fair market price; a key advantage of currency trading.

Because the stock market and other exchange-traded markets only have a fraction of the volume of the currency market, these investors run a greater risk of having wide dealing spreads or large price fluctuations while trading.

No Limit up / limit down in the currency spot market

Under certain price conditions, the number and types of transactions that a futures trader can make are limited. The futures market restricts a trader from initiating new positions and only liquidating existing ones, if the price of a specific currency rises or falls beyond a specific predetermined daily level. This is an artificial way to control daily price volatility. This mechanism is meant to control daily price volatility, but since the futures currency market follows the spot currency market anyway, the next day the futures price can gap up or gap down to readjust to the spot price. In the foreign currency spot market these artificial restrictions are nonexistent, so the trader can trade freely without limitations, applying his trading strategy with stop losses to protect himself from unexpected price fluctuations caused by high volatility.

No short-selling restrictions in currency trading

There are no restrictions to sell currencies short, unlike stocks which have to be sold short on an Uptick rule. This means that with currency trading you can make money just as easily in rising and falling markets. This advantages is especially attractive to currency day traders who want might want to sell a currency short quickly, without any possibility of the trade being delayed by artificial means.

All of these advantages make currency trading superior to stock and futures trading in may ways.

Granville said it best in his book, A Strategy of Daily Stock Market Timing:

“When it’s obvious to the public, it’s obviously wrong.”

Since we talk a lot about sentiment and contrarian sentiment, lets step back and review where this idea came from and how it developed from its origins.

Charles H. Dow
The main principle behind contrarian analysis and sentiment (two sides of the same coin) comes from Charles H. Dow’s work on distribution and accumulation. The same ideas that underpin the Dow Theory. I’m sure you’ll also notice the similarity between these ideas and Weinstein’s stage analysis which breaks up a movement of a security into four parts.

According to Dow Theory, major market movements start with an “accumulation” phase where insiders, and other knowledgeable traders or investors start to buy shares. Since at this point the average public sentiment towards the market is negative, they are able to accumulate shares without significantly pushing prices higher.

Eventually the general sentiment starts to tip as more and more people start to realize that something has changed. This is the stage at which trend followers jump on and start to push up prices further. The trend continues and feeds on itself, perpetuating until it reaches a crescendo.

At this point we reach “distribution”, the final phase of the trend where the reverse happens: insiders, institutions, or if you will, “smart” market participants begin to sell their holdings into a frenzy of indiscriminate public buying. Since a smaller number of players are in the know, their holdings must need be several magnitudes higher than the average retail participant.

This is why we see lopsided sentiment metrics. Since for every trade to occur, we need to have an equal number of shares bought and sold, if the minority are selling, then they must have the ability to supply the demand of the many who are buying (in a distribution phase). If we imagine, for instance, that 90% are bullish, then the average seller must be selling 10 times as large as the average buyer.

Garfield Albee Drew
In the 1940’s, Drew started to gather and study trading statistics from retail brokerage accounts and noticed that small traders or “odd lot” traders tended to sell when the market was bottoming and buy when it was topping. So he started to track odd lot trades on the NYSE and this now familiar metric was born.

I’ve mentioned this sentiment measure a few times before (Climbing the Wall of Worry). There is also the flip side: odd lot short sales ratio. But I suspect that the change in the market structure has eroded the usefulness of odd lot data. When Drew did his studies, odd lot volume was 15% of the NYSE, now it is less than 1%.

Drew garnered attention when he published “New Methods for Profit in the Stock Market” and later started an institutional service (for $95 a year back in the 1960’s) gaining thousands of clients.

Humphrey B. Neil

In 1954, Neil was arguably the first to introduce the concept of contrarian sentiment in his book: The Art of Contrary Thinking. Unfortunately, he didn’t really explain exactly what he meant, other than just doing the opposite of what others are doing.

Neither did he provide any quantitative methods for measuring sentiment to be able to not only put the ideas to the test, but to also come up with a framework that others could follow.

A. W. Cohen
The task of quantification began in 1963 when Cohen started to compile statistics on a number of market newsletters to aggregate their recommendations. It was Cohen who laid the groundwork for moving sentiment and contrarian analysis from vague generalities to hard numbers and metrics. He established a famous sentiment measure that is now known as Investor’s Ingelligence (by ChartCraft) – along with the AAII, the most watched weekly sentiment data.

Cohen began to compile the sentiment data monthly in January 1963. A year later it was measured twice a month and in 1969 it changed to the now familiar weekly frequency. Cohen’s work is now carried on by Michael Burke. Cohen, you may also remember, was the major force behind the popularization of point and figure charting (which has nebulous origins somewhere in the early 1900’s).

R. Earl Hadady
Hadady refined much of the previous work already mentioned, as well as that of his one time partner, J. H. Sibbet – whose most important contribution was weighing each newsletter according to its reach and audience. Hadady delineated methods for both quantitative and qualitative measure of contrary sentiment in his book. He is also the developer of a sentiment measure you’re probably familiar with: Bullish Consensus (now provided by Market Vane).

Although Bullish Consensus is known for its weekly sentiment data on the US equity market, they also track 36 commodity futures markets. Hadady has written other books (both on the market and other subjects) but “Contrary Opinion” remains his masterpiece.

Before becoming Market Vane, Hadady Corp. used to publish charts which plotted sentiment below the major market index. The chart for the S&P 500 Index for 1987 is a great example: on August 25th 1987, Bullish Consensus reached 70% – the critical optimistic level for the first time in the year. On October 20th 1987, Bullish Consensus fell to the critical pessimism level of 25%. Between those two dates, the market provided one of the blackest swans we have ever seen.

The shocking volatility of the 1987 market crash lead Hadady to conclude that weekly numbers were not enough so in late 1988 his company started to compile and disseminate daily Bullish Consensus data.

Granville said it best in his book, A Strategy of Daily Stock Market Timing:

“When it’s obvious to the public, it’s obviously wrong.”

Since we talk a lot about sentiment and contrarian sentiment, lets step back and review where this idea came from and how it developed from its origins.

Charles H. Dow
The main principle behind contrarian analysis and sentiment (two sides of the same coin) comes from Charles H. Dow’s work on distribution and accumulation. The same ideas that underpin the Dow Theory. I’m sure you’ll also notice the similarity between these ideas and Weinstein’s stage analysis which breaks up a movement of a security into four parts.

According to Dow Theory, major market movements start with an “accumulation” phase where insiders, and other knowledgeable traders or investors start to buy shares. Since at this point the average public sentiment towards the market is negative, they are able to accumulate shares without significantly pushing prices higher.

Eventually the general sentiment starts to tip as more and more people start to realize that something has changed. This is the stage at which trend followers jump on and start to push up prices further. The trend continues and feeds on itself, perpetuating until it reaches a crescendo.

At this point we reach “distribution”, the final phase of the trend where the reverse happens: insiders, institutions, or if you will, “smart” market participants begin to sell their holdings into a frenzy of indiscriminate public buying. Since a smaller number of players are in the know, their holdings must need be several magnitudes higher than the average retail participant.

This is why we see lopsided sentiment metrics. Since for every trade to occur, we need to have an equal number of shares bought and sold, if the minority are selling, then they must have the ability to supply the demand of the many who are buying (in a distribution phase). If we imagine, for instance, that 90% are bullish, then the average seller must be selling 10 times as large as the average buyer.

Garfield Albee Drew
In the 1940’s, Drew started to gather and study trading statistics from retail brokerage accounts and noticed that small traders or “odd lot” traders tended to sell when the market was bottoming and buy when it was topping. So he started to track odd lot trades on the NYSE and this now familiar metric was born.

I’ve mentioned this sentiment measure a few times before (Climbing the Wall of Worry). There is also the flip side: odd lot short sales ratio. But I suspect that the change in the market structure has eroded the usefulness of odd lot data. When Drew did his studies, odd lot volume was 15% of the NYSE, now it is less than 1%.

Drew garnered attention when he published “New Methods for Profit in the Stock Market” and later started an institutional service (for $95 a year back in the 1960’s) gaining thousands of clients.

Humphrey B. Neil

In 1954, Neil was arguably the first to introduce the concept of contrarian sentiment in his book: The Art of Contrary Thinking. Unfortunately, he didn’t really explain exactly what he meant, other than just doing the opposite of what others are doing.

Neither did he provide any quantitative methods for measuring sentiment to be able to not only put the ideas to the test, but to also come up with a framework that others could follow.

A. W. Cohen
The task of quantification began in 1963 when Cohen started to compile statistics on a number of market newsletters to aggregate their recommendations. It was Cohen who laid the groundwork for moving sentiment and contrarian analysis from vague generalities to hard numbers and metrics. He established a famous sentiment measure that is now known as Investor’s Ingelligence (by ChartCraft) – along with the AAII, the most watched weekly sentiment data.

Cohen began to compile the sentiment data monthly in January 1963. A year later it was measured twice a month and in 1969 it changed to the now familiar weekly frequency. Cohen’s work is now carried on by Michael Burke. Cohen, you may also remember, was the major force behind the popularization of point and figure charting (which has nebulous origins somewhere in the early 1900’s).

R. Earl Hadady
Hadady refined much of the previous work already mentioned, as well as that of his one time partner, J. H. Sibbet – whose most important contribution was weighing each newsletter according to its reach and audience. Hadady delineated methods for both quantitative and qualitative measure of contrary sentiment in his book. He is also the developer of a sentiment measure you’re probably familiar with: Bullish Consensus (now provided by Market Vane).

Although Bullish Consensus is known for its weekly sentiment data on the US equity market, they also track 36 commodity futures markets. Hadady has written other books (both on the market and other subjects) but “Contrary Opinion” remains his masterpiece.

Before becoming Market Vane, Hadady Corp. used to publish charts which plotted sentiment below the major market index. The chart for the S&P 500 Index for 1987 is a great example: on August 25th 1987, Bullish Consensus reached 70% – the critical optimistic level for the first time in the year. On October 20th 1987, Bullish Consensus fell to the critical pessimism level of 25%. Between those two dates, the market provided one of the blackest swans we have ever seen.

The shocking volatility of the 1987 market crash lead Hadady to conclude that weekly numbers were not enough so in late 1988 his company started to compile and disseminate daily Bullish Consensus data.

Granville said it best in his book, A Strategy of Daily Stock Market Timing:

“When it’s obvious to the public, it’s obviously wrong.”

Since we talk a lot about sentiment and contrarian sentiment, lets step back and review where this idea came from and how it developed from its origins.

Charles H. Dow
The main principle behind contrarian analysis and sentiment (two sides of the same coin) comes from Charles H. Dow’s work on distribution and accumulation. The same ideas that underpin the Dow Theory. I’m sure you’ll also notice the similarity between these ideas and Weinstein’s stage analysis which breaks up a movement of a security into four parts.

According to Dow Theory, major market movements start with an “accumulation” phase where insiders, and other knowledgeable traders or investors start to buy shares. Since at this point the average public sentiment towards the market is negative, they are able to accumulate shares without significantly pushing prices higher.

Eventually the general sentiment starts to tip as more and more people start to realize that something has changed. This is the stage at which trend followers jump on and start to push up prices further. The trend continues and feeds on itself, perpetuating until it reaches a crescendo.

At this point we reach “distribution”, the final phase of the trend where the reverse happens: insiders, institutions, or if you will, “smart” market participants begin to sell their holdings into a frenzy of indiscriminate public buying. Since a smaller number of players are in the know, their holdings must need be several magnitudes higher than the average retail participant.

This is why we see lopsided sentiment metrics. Since for every trade to occur, we need to have an equal number of shares bought and sold, if the minority are selling, then they must have the ability to supply the demand of the many who are buying (in a distribution phase). If we imagine, for instance, that 90% are bullish, then the average seller must be selling 10 times as large as the average buyer.

Garfield Albee Drew
In the 1940’s, Drew started to gather and study trading statistics from retail brokerage accounts and noticed that small traders or “odd lot” traders tended to sell when the market was bottoming and buy when it was topping. So he started to track odd lot trades on the NYSE and this now familiar metric was born.

I’ve mentioned this sentiment measure a few times before (Climbing the Wall of Worry). There is also the flip side: odd lot short sales ratio. But I suspect that the change in the market structure has eroded the usefulness of odd lot data. When Drew did his studies, odd lot volume was 15% of the NYSE, now it is less than 1%.

Drew garnered attention when he published “New Methods for Profit in the Stock Market” and later started an institutional service (for $95 a year back in the 1960’s) gaining thousands of clients.

Humphrey B. Neil

In 1954, Neil was arguably the first to introduce the concept of contrarian sentiment in his book: The Art of Contrary Thinking. Unfortunately, he didn’t really explain exactly what he meant, other than just doing the opposite of what others are doing.

Neither did he provide any quantitative methods for measuring sentiment to be able to not only put the ideas to the test, but to also come up with a framework that others could follow.

A. W. Cohen
The task of quantification began in 1963 when Cohen started to compile statistics on a number of market newsletters to aggregate their recommendations. It was Cohen who laid the groundwork for moving sentiment and contrarian analysis from vague generalities to hard numbers and metrics. He established a famous sentiment measure that is now known as Investor’s Ingelligence (by ChartCraft) – along with the AAII, the most watched weekly sentiment data.

Cohen began to compile the sentiment data monthly in January 1963. A year later it was measured twice a month and in 1969 it changed to the now familiar weekly frequency. Cohen’s work is now carried on by Michael Burke. Cohen, you may also remember, was the major force behind the popularization of point and figure charting (which has nebulous origins somewhere in the early 1900’s).

R. Earl Hadady
Hadady refined much of the previous work already mentioned, as well as that of his one time partner, J. H. Sibbet – whose most important contribution was weighing each newsletter according to its reach and audience. Hadady delineated methods for both quantitative and qualitative measure of contrary sentiment in his book. He is also the developer of a sentiment measure you’re probably familiar with: Bullish Consensus (now provided by Market Vane).

Although Bullish Consensus is known for its weekly sentiment data on the US equity market, they also track 36 commodity futures markets. Hadady has written other books (both on the market and other subjects) but “Contrary Opinion” remains his masterpiece.

Before becoming Market Vane, Hadady Corp. used to publish charts which plotted sentiment below the major market index. The chart for the S&P 500 Index for 1987 is a great example: on August 25th 1987, Bullish Consensus reached 70% – the critical optimistic level for the first time in the year. On October 20th 1987, Bullish Consensus fell to the critical pessimism level of 25%. Between those two dates, the market provided one of the blackest swans we have ever seen.

The shocking volatility of the 1987 market crash lead Hadady to conclude that weekly numbers were not enough so in late 1988 his company started to compile and disseminate daily Bullish Consensus data.

Fundamental analysis of a business involves analyzing its financial statements and health, its management and competitive advantages, and its competitors and markets. When applied to futures and forex, it focuses on the overall state of the economy, interest rates, production, earnings, and management. When analyzing a stock, futures contract, or currency using fundamental analysis there are two basic approaches one can use; bottom up analysis and top down analysis. The term is used to distinguish such analysis from other types of investment analysis, such as quantitative analysis and technical analysis.

Fundamental analysis is performed on historical and present data, but with the goal of making financial forecasts. There are several possible objectives:
to conduct a company stock valuation and predict its probable price evolution,
to make a projection on its business performance,
to evaluate its management and make internal business decisions,
to calculate its credit risk.

Two analytical models

When the objective of the analysis is to determine what stock to buy and at what price, there are two basic methodologies

Fundamental analysis maintains that markets may misprice a security in the short run but that the “correct” price will eventually be reached. Profits can be made by trading the mispriced security and then waiting for the market to recognize its “mistake” and reprice the security.

Technical analysis maintains that all information is reflected already in the stock price. Trends ‘are your friend’ and sentiment changes predate and predict trend changes. Investors’ emotional responses to price movements lead to recognizable price chart patterns. Technical analysis does not care what the ‘value’ of a stock is. Their price predictions are only extrapolations from historical price patterns.

Investors can use both these different but somewhat complementary methods for stock picking. Many fundamental investors use technicals for deciding entry and exit points. Many technical investors use fundamentals to limit their universe of possible stock to ‘good’ companies.

The choice of stock analysis is determined by the investor’s belief in the different paradigms for “how the stock market works”. See the discussions at efficient-market hypothesis, random walk hypothesis, Capital Asset Pricing Model, Fed model Theory of Equity Valuation, Market-based valuation, and Behavioral finance.
Fundamental analysis includes:

1.Economic analysis

2.Industry analysis

3.Company analysis

On the basis of this three analysis the intrinsic value of the shares are determined. This is considered as the true value of the share. If the intrinsic value is higher than the market price it is recommended to buy the share . If it is equal to market price hold the share and if it is less than the market price sell the shares.
[edit]Use by different portfolio styles

Investors may use fundamental analysis within different portfolio management styles.

Buy and hold investors believe that latching onto good businesses allows the investor’s asset to grow with the business. Fundamental analysis lets them find ‘good’ companies, so they lower their risk and probability of wipe-out.
Managers may use fundamental analysis to correctly value ‘good’ and ‘bad’ companies. Even ‘bad’ companies’ stock goes up and down, creating opportunities for profits.

Managers may also consider the economic cycle in determining whether conditions are ‘right’ to buy fundamentally suitable companies.

Contrarian investors distinguish “in the short run, the market is a voting machine, not a weighing machine”. Fundamental analysis allows you to make your own decision on value, and ignore the market.
Value investors restrict their attention to under-valued companies, believing that ‘it’s hard to fall out of a ditch’. The value comes from fundamental analysis.

Managers may use fundamental analysis to determine future growth rates for buying high priced growth stocks.
Managers may also include fundamental factors along with technical factors into computer models (quantitative analysis).
[edit]Top-down and Bottom-up

Investors can use either a top-down or bottom-up approach.

The top-down investor starts his analysis with global economics, including both international and national economic indicators, such as GDP growth rates, inflation, interest rates, exchange rates, productivity, and energy prices. He narrows his search down to regional/industry analysis of total sales, price levels, the effects of competing products, foreign competition, and entry or exit from the industry. Only then does he narrow his search to the best business in that area.

The bottom-up investor starts with specific businesses, regardless of their industry/region.
[edit]Procedures

The analysis of a business’ health starts with financial statement analysis that includes ratios. It looks at dividends paid, operating cash flow, new equity issues and capital financing. The earnings estimates and growth rate projections published widely by Thomson Reuters and others can be considered either ‘fundamental’ (they are facts) or ‘technical’ (they are investor sentiment) based on your perception of their validity.

The determined growth rates (of income and cash) and risk levels (to determine the discount rate) are used in various valuation models. The foremost is the discounted cash flow model, which calculates the present value of the future
dividends received by the investor, along with the eventual sale price. (Gordon model)
earnings of the company, or cash flows of the company.

The amount of debt is also a major consideration in determining a company’s health. It can be quickly assessed using the debt to equity ratio and the current ratio (current assets/current liabilities).

The simple model commonly used is the Price/Earnings ratio. Implicit in this model of a perpetual annuity (Time value of money) is that the ‘flip’ of the P/E is the discount rate appropriate to the risk of the business. The multiple accepted is adjusted for expected growth (that is not built into the model).

Growth estimates are incorporated into the PEG ratio but the math does not hold up to analysis.[neutrality disputed] Its validity depends on the length of time you think the growth will continue.

Computer modelling of stock prices has now replaced much of the subjective interpretation of fundamental data (along with technical data) in the industry. Since about year 2000, with the power of computers to crunch vast quantities of data, a new career has been invented. At some funds (called Quant Funds) the manager’s decisions have been replaced by proprietary mathematical models.