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Hi Talon Yes very wise words indeed. You and Maestro seem to be the two voices on here that make the most sense to me. Well done and thanks. All the Best John
any thoughts on what calculations to use for volatility according to different time frames? example i wouldnt use the same strat on an 8 week swing as i would an intraday trade correct? trying to figure stocks to filter
You might choose to rank (descending highest to lowest) all equities by ADR. Focus on the upper portion of this list. Over time, those equities which no longer 'measure up' will fall off the list as new players move into the top positions. Begin with narrowing down the entire Universe of Equities by culling for quality first. Then, focus on those equities which move (have the highest ADR). Culling criteria: 1. Price $10.00 - $50.00 2. Volume: Greater than 400,000 shares traded daily 3. Float: 5 million to 60 million shares (daily) 4. Positive Earnings 5. Insider Owned Shares > 5% 6. Institutional Owned Shares > 5% You could narrow the list further by choosing to focus on equities which show a cycle of 20% (or more) Price improvement over a period of 6 to 8 days a minimum of five times in the previous six months. HTH. - Spydertrader
While the basics are being addressed, I wanted to ask one additional question regarding data quality. I've spent the last two weeks questioning every data provider I could find (~15 or so) on their methodology for adjusting for cash dividends (for the most part, splits and stock divs are handled consistently across providers). My thinking was that there is only one correct way to do it - adjust for all cash dividends (special and quarterly) in the following manner: 1. Calculate an adjustment factor on the day prior to the ex-date ---> AF = (Close - Div)/Close, then 2. Multiply all prices prior to the ex-date by the AF Do others agree? It was surprising to see that adjusting this way was only an option at two providers. The most common answer was that only special cash dividends are adjusted for (using the steps above), but ordinary quarterly cash dividends are not. That seems inconsistent at best. After some more digging, it appears that this is the format in which the exchanges distribute the data. Most providers seem to filter for bad ticks but then pass it on as-is without further dividend adjustments. Anyhow, a number of times when asking these questions, the representative did not know the answer without consulting their tech people - I guess they're not questions that they get asked often...also surprising. So since Talon previously emphasized the importance of using accurate data, I figured this might be worth highlighting here. Hope its not too far off-topic.
Isn't it ... (?): Expectancy = (Probability of Win * Average Win) - (Probability of Loss * Average Loss) Also ..., in my opinion, your trading shouldn't be "very closely related" to gambling.
Your expectancy formula is correct, and I know many trading books give this formula the way you wrote it. However, that's not the most useful way to think of this if your goal is to build intuition. For instance, one way to think about this is to imagine there are 5 outcomes when you get in a trade: big win, big loss, small win, small loss, breakeven. You roughly assign probabilities to those scenarios and, after doing this a long time, begin to build some intuition about the expectancy of the overall situation. That's just one situation, but don't simplify this to the formula you gave below. Trading has much more to do with gambling than you might think. Why do you think it shouldn't be closely related?
I'm not sure about Spydertrader's answer, but I dont think it answers the question you're asking... perhaps I misunderstand the question you're asking though. To generalize, there are probably two volatility calculations you should be aware of. 1. Average true range. I think everyone already understands this one? 2. Statistical (or historical) volatility. This is the standard deviation of the returns, usually annualized. Note carefully that this calculation works on RETURNS not raw CLOSES. You may use percentage returns or continuously compounded returns without much difference. I would interpret these two measures slightly differently: 1. shows you the average range the market is likely to cover in a single bar. That one's easy. 2. Is probably less intuitive: when this number is correctly annualized, it tells you what a 1 standard deviation price change would be one year from now, assuming normality. It is clearly a measure of close to close volatility, which can be very different than ATR. You should play with these numbers a lot until they make sense. For instance, does it seem right to you that a stock can be bouncing around in a $5 range from 45 - 50, then viciously drop from 50 - 20 in two weeks, and the volatility would actually decrease as that happens? That's a very possible outcome under #2 if the breakdown happens in a straight line (think how closely the closing prices cluster around a best fit line drawn over a fairly short lookback period. Not the same measure, but a useful way to think about it.) Blindly applying these measures will get you in trouble. The behavior of volatility is actually one of the most fascinating aspects of market analysis... and some of the most reliable profits come from systems designed to trade volatility. This is not a topic we can cover adequately in this thread, but it will reward deep study. There are also other volatility measures... many more. For instance, you can calculate measure #2 using the C to O return to study overnight gaps, using L to H return to look at intraday volatility, can look at residuals from a regression line on closes... etc etc etc, but what is more important is what they are measuring rather than how.
FWIW, I disagree with a lot of these criteria, but it depends on what yo're trying to accomplish with the screen.... daytrading? swing trading? investing? longs only? shorts only?