Strategies & Tactics - Portfolio Management and Risk Management Strategies & Tactics. The purpose of this section is to assist in the development and implementation of suitable Strategies & Tactics within the Portfolio Management and Risk Management Process.
A critical component of every Portfolio Management and Risk Management Process is an objective and on-going strategy and tactical adjustment decision making and implementation capability. A foundational part of this capability is the on-going development and maintenance of a disciplined price, interest rate or exchange rate analytic methodology and related historic data bases. The need for this analytic framework and related data bases is created by two opposing forces:
- Market conditions, (i.e., volatility, swing levels, swing points, patterns, sections and phases) are constantly changing and are only clear in retrospect.
- However, portfolio management and risk management related strategic and tactical decisions need to be made and implemented contemporaneous with "real time" price, interest rate, exchange rate, and time movements.
To accommodate an ever-changing financial environment, and to facilitate the portfolio management and risk management process, resources are devoted to the on-going development and refinement of an analytic framework and related historical data base for each major market and instrument involved in creating or intermediating commerce related to the main Global Economic Drivers. The central focus of this effort is to develop a "profile" of the main characteristics exhibited by each market and instrument involved in the portfolio management and risk management process. This is accomplished through the consistent measurement, identification, classification, and comparison of each market and instrument's price, interest rate, or exchange rate swing characteristics.
In summary, the purpose of the Analytic Framework, is to maintain a disciplined analytic methodology and an organized data base, for each market and instrument traded, that allows for systematic evaluations of a given premise and the related conclusion regarding price, interest rate, or exchange rate behavior. This, then allows for the identification of market conditions (set-ups -- phase, section, pattern, swing level, and volatility), wherein, the conclusion operates with reliability, and therefore, suitable strategies and tactics can be developed and profitably implemented.
Back to Top
Rational Analysis - Despite the academic rhetoric about random walk and other market analysis theories, I believe that financial markets and instruments that are freely traded lend themselves to rational analysis.
Volatility - Regardless of price, interest rate, and exchange rate directional movement, prices, interest rates, and exchange rates are constantly changing, sometimes a little, sometimes a lot, sometimes wildly, sometimes quietly. This volatility needs to be measured and utilized to automatically adjust analysis parameters.
Swing Profile - In addition to utilizing a statistical volatility measurement to enable adaptation to changing conditions, the concept of Profiling Swing Characteristics is structured around several other basic premises.
Future Issues - Given my imperfect human nature and understanding of markets, the analytic framework will always be subject to changes resulting from further inquiry into human and market behavior.
Despite the academic rhetoric about random walk and other market analysis theories, I believe that financial markets and instruments that are freely traded lend themselves to rational analysis. And, despite the academic rhetoric about efficient market theory, etc., I also believe that financial markets are simply imperfect emotional human beings interacting with each other in the bidding and offering of securities, commodities, and other instruments. And, as imperfect emotional human beings, the results of their interaction will be imperfect. It is this imperfection that creates opportunities. Just because the market place is, for the most part, an efficient and effective mechanism for the processing and communication of information does not mean the results of that processing and communication are perfect, or even correct.
Human beings, both individually and collectively, tend to establish habit patterns in virtually everything they do. Sometimes, and in some situations, these patterns become well established. However, regardless of how well established the habit pattern may seem, human beings seldom do the same thing in the same way 100% of the time. And, many times the habit pattern tends to evolve. Therefore, a self-adapting probabilistic approach is required to rationally analyze and profile the characteristics exhibited as a result of imperfect emotional human beings interacting with one another in the market place.
Correlation And Probability
Once historic price, interest rate, and exchange rate swings have been cataloged, correlation and probability statistics can be developed utilizing the related price, time, and velocity information (e.g., ranges, cycles, angles, swing points, volume, and degrees) of each swing, within each level. These statistics will allow systematic comparisons between current swing characteristics and previous swing characteristics.
Timely Recognition Of Pattern Transitions
An important focus of this systematic evaluation effort is to enable contemporary recognition of pattern (trend, reversal, consolidation) transitions that, in turn, enable timely adaptations in the suitability of strategies and tactics to the market's phase, section, pattern, swing level, and volatility.
Suitable Strategies And Tactics
This, in turn, will facilitate the development, implementation, and management of strategies and tactics suitable to the typical characteristics of the market and instruments involved in the portfolio management and risk management effort. This will be done within the context of current market conditions, and with the awareness that these conditions will be constantly changing and, therefore, tactical adaptations will always be necessary. Furthermore, suitable strategies and tactics can be developed and implemented for at least four different market dimensions:
- Price, interest rate, or exchange rate movement.
- Relationships (basis, intra and inter market spreads)
Back to Top
Regardless of price, interest rate, and exchange rate directional movement, prices, interest rates, and exchange rates are constantly changing, sometimes a little, sometimes a lot, sometimes wildly, sometimes quietly. This change is oftentimes characterized as volatility. Volatility needs to be understood and measured in such a way as to enable objective categorization of swing movements into significant swing levels. This, in turn, enables the classification of any observable phenomena that can be correlated with price, interest rate, and exchange rate movements. For our purposes, volatility is calculated and expressed as an annualized percentage standard deviation of high and low prices, interest rates, or exchange rates around the closing price, interest rate, or exchange rate of the previous trading day.
Conventional historic volatility measurement tends to focus just on the volatility from close to close. In so doing, much of the volatility that occurs escapes measurement. In an effort to capture all of the volatility that occurs, our volatility measurement is split into three components:
- Above Volatility -- That portion of the volatility that occurs above the referenced closing price, interest rate, or exchange rate.
- Below Volatility -- That portion of the volatility that occurs below the referenced closing price, interest rate, or exchange rate.
- Total Volatility -- The combined volatility that occurs both above and below the referenced closing price, interest rate, or exchange rate.
These volatility measurements have, at least, five useful applications:
- Consistent quantitative measurement of historic swings.
- Heuristic mechanism for adapting systems and sensitivities to evolving market characteristics.
- Determining when the market has gone beyond "normal" volatility.
- Determining when the market is at the extreme boundaries of "normal" volatility.
- Quantifying "normal" risk exposure inherent in a position for purposes of establishing risk control points and position size.
Back to Top
Profiling Swing Characteristics
Volatility is always present and constantly changing. Because of the constantly changing nature of volatility, the profiling of swing characteristics needs to incorporate a self-adapting mechanism. In addition to utilizing a statistical volatility measurement to enable adaptation to changing conditions, the concept of Profiling Swing Characteristics is structured around the notions of:
- Directional price, interest rate, and exchange rate swings.
- Event shocks create swing points (SPs) and cause price, interest rates, and exchange rate swings to change direction.
- Event shock intensity directly affects the magnitude, duration, and velocity characteristics of the resulting swings. This, in turn, enables swings to be classified by degree or level (e.g., Cycle, Major, Intermediate, Minor, and Minute)
- Regardless of level, swings can be further classified as impulsive or corrective.
- Support and resistance zones are created by the clustering of SPs.
- Patterns, sections and phases are created by combinations of swings.
Directional Price, Interest Rate, and Exchange Rate Swings
Prices, interest rates, and exchange rates move in directional swings from one event shock to another.
Price, interest rate, and exchange rate swings are caused by event shocks of varying levels of intensity (e.g., Cycle, Major, Intermediate, Minor, and Minute), to the market. These shocks can be caused by three types of events:
- Indigenous market related events.
- Exogenous events.
- Natural law related events.
Indigenous Market Related Events
These events include periodic government reports, corporate earnings releases, etc. Markets usually discount the perceived effects of these shocks well in advance of official release. Noticeable shocks, related to these type events, occur when reality and discounted perceptions differ. The causes are clear only in retrospect and after the event has been thoroughly hashed over by the public.
These events include war and Acts of God, etc. Some of these events may be discountable to a limited degree, some not at all. Regardless, discounting the timing of event occurrence is difficult. Once the shock occurs, markets tend to discount the effects very quickly and with emotion. Hence, quite significant and volatile swings oftentimes result. Again, the cause and effects are clear only in retrospect after the event has been relegated to the history books.
Natural Law Events
These events tend to be cyclic and non-sensory. Can only see the effects in relation to other observable events. The underlying causes are unknown, but not critical. What is critical, is to objectively observe, measure, catalog, and compare the condition and position of critically related events immediately before, during, and after each swing point; then apply the resulting probabilities to future situations (condition and position of critically related events to swing points) as they are developing. In effect we are looking for correlation more so than causation.
Swing Points (SPs)
As a result of these event shocks, swings originate and terminate at swing points (SPs), wherein, prices, interest rates, and exchange rates reverse from one direction to the opposite direction. There are two types of SPs:
- High Swing Points (HSPs).
- Low Swing Points (LSPs).
High Swing Points (HSPs)
Created when a high price, interest rate, or exchange rate (or multiple equally high prices, interest rates, or exchange rates) is preceded and succeeded by lower highs. INCLUDE ILLUSTRATION.
Low Swing Points (LSPs)
Created when a low price, interest rate, or exchange rate (or multiple equally low prices, interest rates, or exchange rates) is preceded and succeeded by higher lows. INCLUDE ILLUSTRATION.
Significant Swing Levels
Swings are classed into levels of significance by applying a statistical volatility measurement to filter out inconsequential swings and swing points for each of the respective levels. There are five significant swing levels:
- Cycle - Swings that exceed LSPs plus and HSPs minus three standard deviations of the cycle level volatility measurement. The objective is to isolate those swings that tend to last for quarters, and sometimes years, at a time.
- Major -- Swings that exceed LSPs plus and HSPs minus three standard deviations of the major level volatility measurement. The objective is to isolate those swings that tend to last for months, and sometimes quarters, at a time.
- Intermediate -- Swings that exceed LSPs plus and HSPs minus three standard deviations of the intermediate level volatility measurement. The objective is to isolate those swings that tend to last for weeks at a time.
- Minor -- Swings that exceed LSPs plus and HSPs minus three standard deviations of the minor level volatility measurement. The objective is to isolate those swings that tend to last for days at a time.
- Minute - Swings connecting LSPs and HSPs without utilizing a volatility filter. The objective is to isolate those swings that tend to last for partial days or just a few days at a time.
Every swing, regardless of level, moves in either an up or down or down direction. A directional swing is either an impulsive swing or a corrective swing (pattern). This is determined as a function of the swing of the next higher level. Also, each swing, in turn, is generally made up of impulsive and corrective swings (patterns) of the next lower level.
Many of the expressions utilized to further describe impulsive and corrective swings (patterns) are borrowed from The Elliot Wave Theory. However, it must be noted that my use of the descriptive Elliott expressions is because the words aptly describe certain types of swing characteristics. My use of these descriptive expressions is not meant to convey an adherence to the "purist" application of the Theory. Although I have immense respect for, what I understand, the underlying Elliott Wave principles. For a more complete discussion and pure application of the Elliott Wave Theory, you should refer to the original works of R. N. Elliott and the subsequent writings of A. Hamilton Bolton, A. J. Frost, and Robert R. Prechter, Jr.
Impulsive swings are swings in the direction of the swing of the next higher level. There are four types of impulsive swings:
- Only - Occurs when the statistical swing measurement model identifies the same swing points for the swing being measured and the swing of the next higher level. This usually occurs in volatile and extended directional moves (both impulsive and corrective).
- First - Occurs as the first swing within the context of the next higher level swing.
- Mid -- Occurs as a middle swing within the context of the next higher level swing. Some swings of the next higher level have multiple middle swings, while others have no middle swings.
- Last - Occurs as the last swing within the context of the next higher level swing.
Additionally, each of these impulsive swings can be further described as exhibiting normal, extended, or short characteristics for each of their price and time dimensions (or both).
Corrective Swings (Patterns)
Corrective swings (patterns) are swings (patterns) against the direction of the swing of the next higher level. There are two types of corrective swings (patterns):
- Simple -A single corrective swing. Additionally, each simple corrective swing can be further described as exhibiting normal, extended, or short characteristics for each of its price and time dimensions (or both).
- Complex - A pattern of a series of corrective swings. There are five main types of complex corrective patterns. Additionally, each of the swings within a corrective pattern is further labeled as an "A" for first swings, "B" for second swings, "C" for third swings, and so on. The five main types of corrective patterns are:
Swing Type Summary
Each market and instrument tends to exhibit its own unique price, time, speed and relational characteristics for both impulsive and corrective swings (patterns).
Support And Resistance Zones
A support zone is concentrated buying, in sufficient volume, to halt and possibly reverse downward price movement.
A resistance zone is concentrated selling, in sufficient volume, to halt and possibly reverse upward price movement.
Support and resistance zones are typically expressed in terms of historic price, interest rate, and exchange rate levels, wherein, event shocks have created SPs. However, there are several other dimensions and applications of the notion of support and resistance that go well beyond simply saying that because a clustering of SPs occurred at a certain level in the past means that a SP will occur at the same level in the future. Significant support and resistance zones develop around four main elements:
- Price, interest rate, or exchange rate.
- Unique Swing Characteristics.
Significant Price, Interest Rate, and Exchange Rate Support and Resistance Zones
Some historic price, interest rate, and exchange rate levels are more significant as support and resistance zones than others. This can be determined by analyzing the SPs that cluster around historic levels. In addition to counting the total SPs occurring at each level, this analysis classifies the SPs as a function of the significance of the swing level that originated or terminated there. The total number of SPs is important. However, SPs resulting from cycle level swings are obviously more consequential than SPs resulting from minor level swings. Price support and resistance zones are graphically represented by horizontal angles.
Also, the volume of transactions, at a given price level, is an important criterion in determining the power of that price level as a support or resistance zone. More specifically, SPs accompanied by a high transaction volume is more consequential than SPs at the same level accompanied by lower transaction volume.
Significant Time Resistance Zones
When at new high or low price, interest rate, or exchange rate levels, there are no historic SP clusters around which support and resistance zones can be established.
Time cycles may be useful as a measure of resistance to the continuation of a swing, regardless of whether prices are within historic price levels or at new high or low levels. Time resistance zones are graphically represented as vertical angles.
Regardless of whether the market is at new high or low levels, some historic time cycles are more significant, as resistance zones, than others. This can be determined by relating the significance of the time cycles to the swing level significance of the SPs that the time cycles are derived from. In addition to counting the time cycles coming due within each timeframe, this analysis classifies the time cycles as a function of the significance of the swing level the time cycle is measuring. The total number of time cycles coming due is important. However, time cycles resulting from cycle level swings are obviously more consequential than time cycles resulting from minor level swings.
Another important consideration in determining the significance of time cycles coming due is whether the cycle is a quarter, half, or full cycle.
Significant Velocity Support and Resistance Zones
With the introduction of time comes the notion of the combination of price, interest rate, or exchange rate and time. Hence velocity, or price, interest rate, or exchange rate movement over time (rise over run).
Some velocity angles are more significant, as support and resistance zones, than others. Two important considerations come into play in this regard. First, as with both price, interest rates, or exchange rates and time, the significance of the angle is directly related to the swing level significance of the SP that the angle originates from. Naturally, angles originating from cycle level SPs are obviously more consequential than angles originating from minor level SPs. Second, is the angle at which price and time repeatedly travel. This angle, expressed as a ratio of price, interest rate, or exchange rate change per unit of time change (rise over run), can be empirically derived and will be different for each market and instrument, for each time unit (e.g., hour, day, week, month, year), within each swing level (e.g., cycle, major, intermediate, minor, and minute), and for each type of swing (e.g., impulse or corrective). In effect, given its frequent occurrence, this angle will tend to serve as the most significant support and resistance zone.
A standardized framework for analyzing and estimating velocity can be built around the empirically derived significant velocity angle, by employing the notion of balancing units of price, interest rate, or exchange rate movement with units of time movement. The primary method of doing this is to empirically determine the number of price, interest rate, or exchange rate ticks to be included in each pricing unit vs. the time unit being considered, that will result in a 45 degree angle or 1:1 scaling when charting. This will enable standardized comparisons of velocity (similar to Gann's 1:1, 2:1, 4:1, and 8:1 angles).
Velocity support and resistance zones are graphically represented as diagonal angles.
The resulting combination of price, interest rates, or exchange rates, time, and velocity can be used to create squares with angles and divisions that can be useful in determining support and resistance zones, again, regardless of whether prices, interest rates, or exchange rates are within historic price levels or at new high or low levels.
Unique Swing Characteristics As Significant Support And Resistance Zones.
Another notion that can be helpful in establishing support and resistance zones, especially when beyond historic price levels, is the unique characteristics of the swing patterns each market tends to exhibit.
Within each level of significance, impulse and corrective swings create trend, consolidation, and reversal patterns for the next higher level.
Patterns, when combined, come together to create sections of price movement, made up of impulse swings with their companion corrective swings, all within the context of the next higher swing level.
Depending on the individual market, most sustained directional movements tend to have three to five sections.
Combine E&M's, Elliott, and Gann's work on this.
Sections, when combined, come together to create the four basic phases of the Idealized Price, Interest Rate, or Exchange Rate Movement Cycle, e.g., Bull, Top, Bear, and Bottom. (ADD PHASE, SECTION, AND PATTERN DETAIL COMMENTS)
Back to Top
Future Issues to be Addressed
Given my imperfect human nature and understanding of markets, the analytic framework will always be subject to changes resulting from further inquiry into human and market behavior.
- Use total volatility applied to extreme high and low swing
points for swing identification purposes. Will need to determine
how many sigma's.
- Possibly utilize Above and Below (relative to close on day before beginning of range utilized in calculating volatility) separately for giving probabalistic warning of swing changes. Will need to develop probabilities around how often a one sigma violation leads to a two or three sigma swing indication measured from an extreme high or low reference point.
- Also, compare Above volatility to Call premium implied volatility,
and Below volatility to Put premium implied volatility.
- Implied calculated from Options. Problematic when need long
term history, given that options have not been around as long
as most of the underlying instruments we will trade.
- Review option pricing models.
- McMillan (Ppg 457-474).
- McMillan (Ppg 727- ) Impact on strategy -- overall, sell high
volatility and buy low volatility
- Comparison of historic vs. implied.
- Comparison of implied vs. implied.
- Although price, interest rate, and exchange rate levels are
numbered on the vertical axis of a chart, the pricing divisions
of a square are drawn as horizontal angles. Price, interest rates,
and exchange rates are also represented as a latitudinal degree
when related to planetary movements.
- Although time increments are numbered on the horizontal axis
of a chart, the time divisions of a square are drawn as vertical
angles. Time is also represented as a longitudinal degree when
related to planetary movements.
- Note the date, and the number of days after the SP, a given
swing was recognized. Compare this date to other indications.
- Quarter cycles.
- Half cycles.
- Full cycles.
- Represented as a diagonal angle reflecting the speed of price
over time (rise over run).
- Produce historic angles that can be useful in evaluating velocity
of current angles, and establishing appropriate chart scaling,
at each swing level.
- Note: Will need to initiate (at 0) both a price and time count
(calendar and trading) at the first swing point to facilitate
delta and variance calculations utilizing SP information from
several swings, potentially, all from different times and price
- Also, review regression analysis, esp., delta and variance
(error of the estimate) about delta.
- Also pursue model measuring non-linear or curvilinear regression.
- Best fit velocity angle connecting lows or LSPs (support),
and highs or HSPs (resistance) with variance range.
- Best fit of all data points (high, low, close) in up-swing.
- Develop ratio of each swing's velocity to previous swings
velocity (especially useful when current swing is a corrective
- Within each swing level, classify swings as a function of
the pattern(s) of which they are a part.
- Within each swing level, determine typical number of sections
(usually 3-4 impulse swings with companion corrective swings)
that tend to make up swings in the next higher level.
- See Gann's Commodities, pg. 50., and Course.
- Relate to the type of reversal pattern, of the next higher
level, the swings making up the sections from which they originate.
- Significant swing points within each swing level and as a
function of duration of swings in that level. See Gann's Commodities,
pg. 316-317. Trend line charts. See Gann's Course, pg ?.
Application of Framework and Data Base
Given the assumption that volatility is an inherent part of price
movement (regardless of phase, section, pattern, swing level,
or price level), and, given that volatility levels do change over
time, it is important to view the significance of any given price
movement, over any given timeframe, at any given swing level,
within any given pattern, and within any given section of a specific
phase, relative to "normal" for the same reference points.
Common Query Patterns
Support And Resistance Zones
- These queries are made as a function of the preceding swing's
range, at each swing level. The results are applied to the future
time period associated with the squaring (and related angles and
divisions) of that swing. The purpose is to estimate where the
strongest price, time, and velocity support and resistance zones
Price, Interest Rate, or Exchange Rate
- Clustering of historic HSPs and LSPs, from all swing levels,
within the range of the square of the preceding swing. Clusters
above serve as resistance. Below serve as support.
- Histogram all SPs within range of previous swing.
- Compare to 1/8, 1/3 and Fibonacci division levels.
- Also, develop statistics around each cluster. This, in combination
with angles, cycles, and swing objectives, should give some insight
into which pricing divisions, within the square, could be the
- Weight each SP by volume and significance of swing level.
- Review Gann's Course pg., 78.
- Clustering of all time cycles (quarter, half, and full), from
all swing levels, coming due in the timeframe resulting from squaring
the preceding swing represents resistance.
- Histogram all time cycles (quarter, half, and full) from all
swing levels to gain a sense of which time divisions, in the square,
could be expected to provide greatest time resistance.
- Clustering of velocity support and resistance angles within
the same swing level, and within the price range of the square
of the preceding swing.
- Clusters above current prices and in front of current time
is resistance (velocity resistance angle). Clusters below current
prices and in front of current time is support (velocity support
- Combine clusters of price and time support and resistance
levels with significant angles originating from HSPs, LSPs, range,
Other Support And Resistance Queries
Percent Movement Of Previous Swings
- Measure each swing point as a percent of next higher swing
level's HSP, LSP, and range (as per Gann's Commodities, pg. 32-34)
to determine statistical modes (histogram), averages, and standard
deviations. This should provide a handle on where swing points
typically cluster, as a percent of the preceding swing, for each
market and instrument. Compare this to fixed divisions of squares
to make specific application instead of applying fixed percentages
regardless of market or instrument.
- Velocity Over Time Equals Price -- Given velocity and number
of time units in past similar swings, estimate price, interest
rate, or exchange rate support or resistance levels for current
- Velocity Over Price Equals Time -- Also, given velocity and
price, interest rate, or exchange rate level resulting from %
of previous swing measures, estimate time resistance for current
- See McLaren pg. 17.
- For all historic and estimated support and resistance levels,
need to determine the mode (histogram), mean, and standard deviations
of false breakouts and penetrations through those levels for "lost
motion" stop loss placement.
Forecast Market Conditions
- Make statement regarding phase, section, pattern, and swing
level; then search for all swings that fit.
Forecast Swing Characteristics
- Make statement regarding characteristics of current swing
(e.g., price range, time span, and/or velocity) and search for
phase, section, pattern, swing levels that those swing characteristics
have appeared in.
- Conditions that provide insight into pattern (trend, reversal,
consolidation) transitions, while they are occurring, within each
swing level. The key is to objectively observe, measure, catalog,
and compare the condition and position of critical related events
immediately before, during, and after each swing point and related
swings of each level; then apply the resulting probabilities to
future situations (condition and position of critical related
events to swing points and related swings) as they are developing.
In effect we are looking for correlation more so than causation;
(see Gann's Commodities, pg. 51, 63+, 309, 312).
- Also, need to develop and add to Strategy Selection and Implementation
sections the results from the following inquiries regarding leading,
coincident, and lagging transition indications:
- Section Maturity -- For each swing level (3rd or 5th).
- Overbalancing -- Impulse swing relative to preceding impulse
- Price, interest rate, or exchange rate.
- Exhaustion gaps.
- Reversal days
- Breakaway gaps
- Overbalancing -- Corrective swing relative to previous corrective
- Break-outs And Penetrations:
- Nearby swing points of same swing level.
- Support and resistance zones after remaining in a trading
range for extended period.
- Pull-backs And Throwbacks.
Critical Relationships Around Swing Points
Immediately before, during, and after swing points at each level,
observe the position and condition of:
- Open Interest
- True Range Volatility Levels
- Significant Calendar Elements
- Day of Week
- Day of Month
- Time cycles
- Back 360
- Significant squares
- Planetary positions and cycles
Overbalancing Of Price, Time, And Velocity
For each swing, compare the price, interest rate, or exchange
rate range, time span, and velocity to previous similar swings,
within the next higher swing level, to detect an overbalancing
(or continued balancing) of price, interest rate, or exchange
rate, time, and velocity. See McLaren, pg., 17; Gann's Commodities,
pg. 23-25, 32-33, and Course, pg.;
- Impulse Swings -- The emphasis is on determining the nature
of rallies in up swings and the nature of declines in down swings
within each swing level. When the price, interest rate, or exchange
rate range and time span of the current rally or decline is shorter
and the velocity is slower, than the normal nature for that phase,
section, pattern, and swing level, be alert to impending change.
- Corrective Swings -- The emphasis is on determining the nature
of rallies in down swings and the nature of declines in up swings
within each swing level. When the price, interest rate, or exchange
rate range and time span of the current rally or decline extends
and the velocity accelerates beyond the normal nature for that
phase, section, pattern, swing level, be alert that change has
started to occur.
Fibonacci Summation Series (FSS)
Application of FSS to price, time, and velocity analysis. Need
to think in non-linear terms.
Percent Of Previous Moves
- Similar Past Moves
- Determine, in scaling price to time, if need 1:1 (45 degree);
or Golden Spiral 1+:1.
- Relate shock (and related wave) concept to Swing Points (SP),
impulse and corrective, at each level; Golden Spiral to time and
price movements after shock SPs.
Arc To Diameter
- Determine relationships between arc to diameter and Golden
Section ratio (1.618) and time and price movement ratios. Refer
to Frost & Prechter's work on Elliott (pg. 88 and 178+)
- Sequence of Golden Section to Golden Rectangle to Golden Spiral
to Circle divided into quadrants. Refer to Frost & Prechter's
work on Elliott (pg. 73-101). Questions; Converting points on
arc to linear point.
- As arc of spiral moves into another quadrant; Translate price
and time points and relationships in (Golden Spiral) sequential
quadrants to one price and time quadrant, absolute and relative.
- Translate price and time scale to move along Golden Spiral
through sequential quadrants.
Gann And FSS
- Relate Gann's velocity angles to FSS reactions. As get further
away from swing points angles originate from, especially when
price is above the faster angles, have more room for a greater
percentage correction (in next lower swing level) and still stay
within higher swing level.
- FSS in planetary and sunspot cycles.
Other FSS and Cycle Aspects To Analyze
- Time cycle based on 360 degree circle.
- Sum of all angles in square (all four-sided objects) equal
360 degrees, therefore, equal cycle.
- Sum of all angles in triangle equal 180 degrees, therefore,
equal half cycle.
- Question -- Can a section of price movement, an impulse swing
plus companion correction swing, plus line between swing point
initiating impulse swing and swing point terminating correction
swing be considered a half cycle given that a triangle is formed?
- A swing, either impulse or correction, equal quarter cycle.
- Question -- If know sides making up 3 of the angles of a four
sided object, should be able to determine remaining angle and
- Relate to swirling squares and rectangles from FSS
Other Gann Issues
- Gann Course pg., 61 Equate par with 360 degrees each point
in price would be equal to 3.6 degrees relate to latitudes
- Look at Gann for comments characterizing a market as either
strong or weak.
- Strong -- After extended decline, ascending LSPs and HSPs.
- Weak -- After extended advance, descending HSPs and LSPs.
- Test All Of Gann's Course Rules
- Overbalancing price and time in combination with swing point
- Retracement percentages (mode, mean, standard deviation).
Histograms and distribution curves.
- Pyramiding in long-pull swings.
- 3-D chart proving relation between price and volume which
shows velocity or speed and trend.
- Determine if there is a need to segment normal price moves
by price level, and normal time moves by sections of phases, and
normal angles by price levels and/or sections.
- Basis Relationships
- Yield Curve
- Put/Call ratios
- Include information on volatility during swings (at each level?)
for identifying suitability of buying or selling option premiums.
- Periodic industry events that tend to influence price movement
(e.g., Treasury refundings). Relate event to price movement within
context of phase, section, pattern, swing level and volatility.
- Scaling and 45 Degree Angle -- What is the significant relationship
and/or event the 45 degree angle is best fitted to represent.
1:1 price to time relationship -- One unit of price to one unit
of time (regardless of the number of ticks in a price unit and
span of time in a time unit).
- Velocity Support And Resistance Angles -- Question becomes
which swing level (Cycle, Major, Intermediate, Minor) should be
used to determine the average velocity angle for use in scaling
- Given that a swing is a quarter cycle or 90 degrees, and that
90 days is a quarter of a year, try using the Square of 90 to
test squaring of price and time. Use Back 360 function to check
- See Gann's Course, pg. 48.
- Also, experiment with squaring as a function of the normal
time spent in the impulse swings of the next higher swing level.
See if there are any close relationships (either multiples or
fractions) between this time issue and the more commonly used
squares Gann employed. Also, do the same as the foregoing on corrective
- Volume and Open Interest Characteristics -- Within each swing
level, relative to same swing and previous similar swings.
- Manias And Panics -- Flight to quality in panics; flight to
risk in manias.
- Ag Commodities -- In ag commodity markets also consider each
contract month as a separate instrument with its own characteristic
set. Also focus on key seasonal dates (e.g., planting, maturity,
- Regression Analysis -- Delta and variance about delta.
- Pursue modeling measuring nonlinear or curvilinear regression.
For an excellent series of papers related to this, see Prof. Lester Ingber's
archives, some viewable as a series of .gif files. Prof Ingber
gives motivation for his statistical mechanics approach to deal
with problems of presenting nonlinear multivariate analyses faithful
to the intuition of decision-makers: "For at least a large
class of systems, these problems can be bypassed by using a blend
of an intuitive and powerful mathematical-physics formalism to
generate 'canonical momenta' indicators (CMI), which are used
by AI-type rule-based models of management of complex systems.
Typically, both the formalism generating the CMI and the rule-based
models have quite nonlinear constructs, and they must be 'trained'
or fit to data subsequent to testing on 'out-of-sample' data,
before they can be used effectively for 'real-time' production
runs. To handle these fits of nonlinear models of real-world data,
a generic powerful optimization code, Adaptive Simulated Annealing
(ASA), has been developed."
Back to Top