Do Or Die

As a counter-view F has been in a downtrend. You can see that it has been coming down since last 6 months or so. It's RS peaked out in April and shows no improvement. You might have remembered AOL, we discussed here and here. It was similar to F and broke down. I had the view that AOL should only be bought after breakout from range which it never did. (BTW the other two 'survivor' stocks after crash AAPL and KO have gone up nicely) The point I want to make is divergence signal a reversal and as such more often initiate a trade against current trend. Hence it is good to look for some confirmation. Buy above 11.00 (upper part of range, near breakout), not immediately.

Scaling-in or continuity trades means buying in parts at different prices rather than buying all at one price level. For example, you buy half shares at current price and half shares (anticipatory) near 6.00 which is historically important support. Or, 40% shares at current price, 30% at 8.0 and 30% at 6.0. You can still maintain a hard-stop of 5.0 (say) for all of your position. With scaling-in, If the stock is dragged down by overall weak market, you are not stopped out. Even if it flies off from here you still have nearly half shares so it means good profit. Similarly scaling out means selling your shares in parts at different price levels rather than selling all at stoploss or profit target. For example, suppose your stock hits the profit target and you book profit on all shares. Sometimes the stock will continue to run upwards and you miss part of profits. An example of scaling out will be to use profit target for half of position and use trailing stop for other half of position. If you plan to hold a trade for more than 100 bars (say) this soothes the equity curve to a good extent. You can backtest on what difference it makes. This is one of the simplest approach to trade management, and you should be able to learn quickly. A more 'advanced approach' is to buy/sell each time the market gives you information about upcoming moves, discussed as the 'combination approach' in this article: http://www.traderslaboratory.com/forums/psychology/10512-sin-predicting-anticipatory-trading.html

XLU is back to 3-year highs. The overall market is much more stabilized with upwards bias. Markets have been up 5 days in a row so there is room for correction immediately.

It can help you time the market with great precision, see here a live example. Yes it is a good approach. F is definitely a great pick, actually it looks too cheesy to work :p. For investing with horizon of >6 months, half can be bought here with stoploss 9.80. Why half size? for trading on longer time frames, scaling-in works better than stop loss and scaling out works better than profit targets.

I agree... programming skills are more important than maths unless you are applying for trading jobs. Programming itself requires to have great organization skills and ability to create processes, manage variables.

I will recommend the book "Trading Regime Analysis: The Probability of Volatility" for discretionary traders. The good part is that it clarifies the concepts and provides many ideas. The other side is that the book is verbose.

Dr. Howard, welcome to TL! I'm a amibroker user and benefited from your work. Will look forward to hear from you more.

Hi Zdo, I'm not sure if I understand you correctly. The way I define a 'trading regime' is- a phase when particular strategy consistently outperforms/underperforms. Trading regimes can be identified in certain time intervals and certain instruments. In this market approach- indicators, patterns, and methods of analysis are just 'windows' to see the market. For example take the 20 period MA- if it's sloping upwards you can say market is trending upwards; if prices are too far from the MA you can say they are due for correction and so on. A trading strategy can formed with this window with buy-sell rules. So a trading regime is a phase when a particular window captures most of price behavior, and its associated strategy is significantly profitable. The strategy itself could be trend following, mean reversion, momentum buying, breakouts and so on. For example, some stocks will show more profitability in breakout trading than others. This can be backtested per 100 breakouts on 15 min time frame (say). Gong away from a mean or coming back to mean should solely depend upon how the mean is defined. A trading regime can be defined and backtested based on this analysis method.

There isn't any stock 'universally' dormant.... and every stock may be dormant for a group of traders who perceive an uninteresting risk/reward in a certain phase. Trading regimes can be put in two major categories: Trend Following Vs Mean Reversion

Old news but relevant: Currency Trading | Foreign currency trading is an easy way to lose money - Los Angeles Times An estimated 615,000 Americans are dabbling in foreign currency trading... At FXCM, 75% to 77% of customers lost money each quarter last year, according to newly required disclosures to the Commodity Futures Trading Commission. At Gain... the number of unprofitable customers hovered between 72% and 79% every quarter last year, according to its filing. FXCM made $2,641 for every active trader, while the average customer had $3,658. The NFA complaint alleged that Gain set its trading program to allow certain trades when they went in Gain's favor, while not allowing the same trades when they went in the customer's favor, costing clients $169,502 during one three-month stretch in 2009.

Maybe we are overlapping things. "Huge profits come from being at the right place at the right time." You need a setup with positive expectancy to accomplish this. You cannot simple make profits by buying out anywhere and following 'money management'. Timing and money management are two totally different things. Timing is achieved through selection of non-random pattens only (your 'edge'). Timing has no meaning in purely random data.. Money management can maximize your productivity, not create it. Again this is the same reason mathematicians do not rule the market. Try backtesting same system on random data and real data- while considering slippage and commissions.

The Utilities are still strong- XLU trading close to 40 day moving average, and the average is slightly sloping upwards. The Financials are the best bet for directional trading, because it is leading the overall market.

Previous article in this series are: Trend Following Vs Mean Reversion: Trading Regimes, Introduction to Understanding Volatility, Trading Regime Analysis Using RSI, Trading Regime Analysis Using RWI, Trading Regime Analysis Using Chart Patterns- Part 1, and Trading Regime Analysis Using Chart Patterns- Part 2. People spend most of the time trying to ‘find’ a better indicator but it is ironic that very little time is spent to analyze phases in which a particular indicator works well and how these phases change. Stationarity is the statistician’s term for regimes in price behavior. It is defined as a lookback period for which the averages and other statistics (e.g. flux densities, variances) do not change over time. For example, range-bound markets where tops and bottom tend to appear after predictable drift. Non-stationarity implies that the characteristics of sample data change from time to time. Now the fact is that markets move in cycles of low-stationarity to high-stationarity. To expect an indicator (for example, moving average or ‘double top’) to perform consistently over time is a capital mistake because it assumes stationarity in market prices. If you study a pattern’s forecasting ability in 2010 and expect that ability to remain same in 2011 irrespective of market characteristics in 2011, it simply does not make sense. There is a fundamental uncertainty in price behavior. You can never tell if the trading regime will shift with your next trade (except ofcourse, if you are a vendor who needs to prove his accuracy). This is also why trade/risk management becomes very important- ability to handle losing trades. From the technical analysis side, it also implies the ability to quickly ‘adjust’ to the market. So if I’m using a strategy- say the ubiquitous moving average crossover, the consistency of its profitability depends upon the stationarity cycle of the market. The ‘amount’ of stationarity over a lookback period depends over time frames and instruments. For example, stationarity on intraday time frames in stocks is far less than stationarity on intraday time frame in DJI. In general, stationarity on EOD time frames tends to more than the stationarity on intraday timeframes. It makes sense to chose a time frame on which you are quick to identify the phases of high stationarity. Measuring Stationarity and Identifying Stable Stationary Cycles For day-to-day trading, the focus is to find longest lookback period for which the market exhibits high amount of stationarity. Now the first important question is, how you measure stationarity. Dr. Steenbarger suggests the ‘dirty’ t-test which can be achieved in MS Excel via TTEST function. T-test is a poor approximation for stationarity, but a good start. You can divide the time series into two halves and see how closer the t-test is to zero (the distributions are similar during that lookback period). The process will be repetitive to find the optimal window for a phase of high stationarity. Once you have the window, confirm the behavior of a trading indicator/strategy during that time. Check if they respond well to price swing highs and lows and the optimal stops/drawdown for such strategy. Finally, start trading with this strategy until the profitability starts coming down (which means the trading regime is shifting). As previously questioned, some may think this is curve-fitting. To quote Dr. Steenbarger: Using historical backtests you can determine regimes in which you strategy works best. For example, test over 10,000 bars of history to test how long and how frequent were the windows in which the strategy trades profitability. This increases your confidence in strategy and your task remains to identify (future) regime phases in which your strategy works good. The next important question is to identify short-term non-stationarities which indicate a regime change. This depends on how you measure stationarity, as well the particular regimes you want to identify. Compare this chart to the third chart in Trading Regime Analysis Using RSI

Posting tags looks really boring I think I will list my good stuff here and link it in my signature.

Profit comes from positive expectancy. Trading is a negative sum game. This is not about the question of disagreeing, I'm a little surprised because this is the first time I've heard such perception.

If this were the case mathematicians would turn out as best traders. You cannot make money UNLESS you are able to identify patterns with positive expectancy (non-random).

http://www.traderslaboratory.com/forums/technical-analysis/10720-relative-strength-resources-glossary.html#post127409

Totally agree. BTW I prepared excel sheet which shows RSI Divergence in random data several years ago. I see a discussion going here: http://www.traderslaboratory.com/forums/beginners-forum/10734-divergence-trading.html#post127462 which so far adds no value against the divergence found in random data (i.e. is no positive expectancy, no edge). Maybe I will add that spreadsheet in this thread once divergence has been sufficiently discussed on real data. The most important thing I learned from studying random charts was- If anything looks too easy, it does not works; if anything can be written down in one-liner rule, it does not works.

But again think of it.... who really trades on just a double top. Even those who trade on chart patterns support their decision by a number of discretionary factors: candlestick shapes, volumes, trend, time frame confluence and so on. BTW by 'trader' I mean a 'profitable trader' LOL. Probably the lesson here is for newbies that anything which is too easy does not works.

Patterns appear in random data, but they are 'random' In the candlestick chart attached few posts back; between bars 126-131 you see bars suddenly go 1/4th of their size. In reality the volatility does not changes so abruptly... it undergoes cycles of expansion and contraction. In the second candlestick chart in this post: http://www.traderslaboratory.com/forums/technical-analysis/10728-question-randomness.html#post127386 you can see a reverse-hammer,hammer,reverse-hammer sequence which I cannot recall on a real chart (between bars 1-16).

First post on TL and the post starts with his achievement and 'coaching' ability. Even though this person may not be phishing anything, such showy attitude leaves a bad feeling

Empirical Research The academic community has dumped most of technical analysis techniques as voodoo; however, RS has been validated repeatedly. One-month Individual Stock Return Reversals and Industry Return Momentum Marc W. Simpson, Emiliano Giudici, John T. Emery August, 2011 That is, a strategy that buys the losers within the previous month’s winning industry and shorts the winners in the previous month’s losing industry significantly outperforms an overreaction-based strategy that simply buys losers and shorts winners in the market overall, and it outperforms a industry-momentum-based strategy that simply buys the previous month’s winning industry portfolio and shorts the previous month’s losing industry portfolio. Optimal Momentum: A Global Cross Asset Approach Gary Antonacci, July, 2011 Fixed income securities become active in the portfolio only when they exhibit stronger momentum than equities... Practitioners sometimes call it relative strength investing.The results are extraordinary risk adjusted returns at a reasonable level of volatility. Industries and Stock Return Reversals Allaudeen Hameed, Joshua Huang, G. Mujtaba Mian March, 2010 A return-based trading strategy that capitalizes on the inter-industry momentum and intra-industry reversals produces a monthly, risk-adjusted return of 2 percent. What Does Equity Sector Orderflow Tell Us about the Economy? Alessandro Beber, Michael W. Brandt and Kenneth A. Kavajecz June 2008 The empirical foot-print of sector rotation has predictive power for the evolution of the economy, future stock market returns, and future bond market returns, even after controlling for relative sector returns. The Halloween Effect in US Sectors Ben Jacobsen, Nuttawat Visaltanachoti May, 2006 Halloween effect…is almost absent in sectors related to consumer consumption but is strong in production sectors. There are, large differences across sectors and industries. Cross-Industry Momentum Lior Menzly, Oguzhan Ozbas February 2006 Trading strategies that consist of simultaneously buying and selling industries with respectively high and low returns in upstream or downstream industries over the previous month yield significant profits. Momentum Narasimhan Jegadeesh, Sheridan Titman October, 2001 There is substantial evidence that indicates that stocks that perform the best (worst) over a three to 12 month period tend to continue to perform well (poorly) over the subsequent three to 12 months. Predicting Stock Returns Using Industry-Relative Firm Characteristics Clifford S. Asness , R. Burt Porter , Ross L. Stevens February, 2000 We find that within-industry momentum (i.e., the firm's past return less the industry average return) has predictive power for the firm's stock return beyond that captured by across-industry momentum. Do Industries Explain Momentum? Tobias J. Moskowitz , Mark Grinblatt March 1999 Iindustry momentum investment strategies, which buy stocks from past winning industries and sell stocks from past losing industries, appear highly profitable, even after controlling for size, book-to-market equity, individual stock momentum, the cross-sectional dispersion in mean returns, and potential microstructure influences.

All my charts are EOD only: http://www.traderslaboratory.com/forums/technical-analysis/10720-relative-strength-resources-glossary.html

I'm not sure which formula you used to calculate the candlesticks... but if you calculate each of O/H/L/C totally independent from any previous data, the candlesticks get pretty messed up.

When I used to trade on floor, a friend once came with several candlestick charts which were generated out of random data. They were intimidating at first, but we all came to the conclusion that none of the classic chart patterns can be seen on random charts. For example, a extended up run which culminates in 'reversal bar', 'long-legged doji' or 'engulfing bear'. BTW, could you please post a chart with minimum 100 bars... 20 bars is too short of 'price' history.