Bollinger Bands M1.
Bollinger Bands Scalping Forex System. Bollinger Bands Scalping Trading System - Forex Strategies - Forex Resources - Forex Trading-free forex trading signals and FX Forecast Free Forex Strategies, Forex indicators, forex resources and free forex forecast. Bollinger uses these various M patterns with Bollinger Bands to identify M-Tops. According to Bollinger, tops are usually more complicated and drawn out than bottoms. Double tops, head-and-shoulders patterns, and diamonds represent evolving tops.
In Chester Keltner proposed a trading system, The Day Moving Average Rule, which later became Keltner bands in the hands of market technicians whose names we do not know. Next comes the work of J. Hurst who used cycles to draw envelopes around the price structure. Hurst's work was so elegant that it became a sort of grail with many trying to replicate it, but few succeeding. In the early '70s percentage bands became very popular, though we have no idea who created them. They were simply a moving average shifted up and down by a user-specified percent.
Percentage bands had the decided advantage of being easy to deploy by hand. Arthur Merrill suggested multiply and dividing by one plus the desired percentage. When I started using trading bands percentage bands were the most popular bands by far. Along the way we got another fine example of envelopes, Donchian bands, which consist of the highest high and lowest low of the immediately prior n-days. Over the years there have been many variations on those ideas, some of which are still in use.
Today the most popular approaches to trading bands are Donchian, Keltner, Percentage and, of course, Bollinger Bands. Percentage bands are fixed, they do not adapt to changing market conditions; Donchian bands use recent highs and lows and Keltner bands use Average True Range as adaptive mechanisms. Bollinger Bands use standard deviation to adapt to changing market conditions and thereby hangs a tale.
When I became active in the markets on a full time basis in I was mainly interested in options and technical analysis. Information on both was hard to obtain in those days but I persisted; with the help of an early microcomputer I was able to make some progress.
A touch of the upper band by price that was not confirmed by strength in the oscillator was a sell setup and a similarly unconfirmed tag of the lower band was a buy setup. The problem with that approach was that percentage bands needed to be adjusted over time to keep them germane to the price structure and the adjustment process let emotions into the analytical process.
If you were bullish, you had a natural tendency to draw the bands so they presented a bullish picture, if you were bearish the natural result was a picture with a bearish bias. This was clearly a problem. We tried reset rules like lookbacks with some success, but what we really needed was an adaptive mechanism.
I was trading options at the time and had built some volatility models in an early spreadsheet program called SuperCalc. One day I copied a volatility formula down a column of data and noticed that volatility was changing over time. Seeing that, I wondered if volatility couldn't be used to set the width of trading bands.
That idea may seem obvious now, but at the time it was a leap of faith. At that time volatility was thought to be a static quantity, a property of a security, and that if it changed at all, it did so only in a very long-term sense, over the life of a company for example.
Today we know the volatility is a dynamic quantity, indeed very dynamic. In a strong uptrend, prices can walk up the upper band and rarely touch the lower band.
Conversely, prices can walk down the lower band and rarely touch the upper band in a strong downtrend. This represents a move that is 2 standard deviations below the day moving average. The oversold readings in early July and early November provided good entry points to partake in the bigger uptrend green arrows. MFI is bound between zero and one hundred.
This uptrend was subsequently affirmed with two more signals in early September and mid-November. While these signals were good for trend identification, traders would likely have had issues with the risk-reward ratio after such big moves. Traders might consider using this method to identify the trend and then look for appropriate overbought or oversold levels for better entry points.
Surges towards the upper band show strength, but can sometimes be interpreted as overbought. Plunges to the lower band show weakness, but can sometimes be interpreted as oversold. A lot depends on the underlying trend and other indicators. Click here for a live chart. The default parameters 20,2 are based on the default parameters for Bollinger Bands. These can be changed accordingly. According to Bollinger, these stocks could be starting new up swings.
Bollinger recommends making small incremental adjustments to the standard deviation multiplier.
Pullbacks were shallow as Apple reversed well above the lower band and resumed its uptrend.