ADOBE PRE-EARNINGS OPTION TRADES -SUMMARY

EQUITY in this ledger: Adobe (NASDAQ:ADBE)

Introducing our move toward a “block” “chain” model using Excel for baseline configuration of input/output parameters regarding Option Trading.

  1. Block: CODEX.  C-RTN, Spread (Strangle), “BUY” (Calculated signal),  Profit/Loss
  2. Excel Layout – Call/Put Workbook Inputs
  3. Determining Option Chain, Strike Price, Premium correlations
  4. Macroaxis Finance:  ADBE Recommendation Graphic

Taking into account the opportunity to make profits from the earnings volatility, we review out context for the pre-earnings report for the asset Adobe (NASDAQ: ADBE).

  1. CODEX/C-RTN BLOCK:  This is a signal based calculation that takes into account both underlying price ranges and statistical computations.  It is sensitive to price move, just as Beta, even more so, and provides a Forward-Looking outcome.  When CODEX is positive than the underlying price trend is Bullish.  When it moves into the negative, the underlying price trend is Bearish.  The max number either way is +1.000 or -1.000.   C-RTN represents a modification of monetized cyclic period to determine the trend, and a calibrated signal “BUY” that is based on a Logic formula.   When the Codex and C-RTN are in conflict, meaning they don’t complement either positive or negative outputs, than there is a risk averse alert for entry.
  2. The CALL/PUT “BLOCK”  aligns the correlated data inputs provided to track the variances between elements.   The Excel layout is flexible so that the elements can be arranged in accordance to the option trader’s hypothesis.  We use “Thinkorswim” to export the selected option chain(s) that is pasted into adjacent spreadsheet.  Then the relevant data is cut and pasted into this format.   What is not shown is the DTE  (Time Decay) entry price BLOCK that provides the “limit” order price in relationship to the traders spread.  You’ll notice the Entry Price input where, in this case, we went Out-of-the-Money (OTM) for both Long Call/Put.  Typically, we prefer a comparable premium, yet our strategy for this trade was short term, capitalizing on volatility and time decay.
  3. BLOCK ARCHITECTURE for this layout is meant to be an optical reference; both for mental entrainment by seeing the correlation of the underlying price move to the Options inputs, and providing an orientation to see time decay of the premiums in real time.

Our terminology has changed over many times over the years.  Conceptually, it’s basically all the same:  building a node of dependent statistical input data that is available to be “chained” or “linked” or “neural” to other “clustered” independent data that is incorporated into various Excel formulas.  We like the flexibility of using Excel as it gives us many options for simulating outcomes and most importantly Proof of Concept.

This ledger publication is extremely simplified in presentation.  As we present more of our own trades and the context behind them, things will become sophisticated and in-depth.

 

CODEX C-RTN P/L
0.07804 -0.0162 $72.00
STRANGLE BUY Signal
CALL PUT
Month 16 SEP 16 (4) 100
Trade Date 09/12/16 09/12/16
Strike 100 95
Premium 3.05 2.13
Ask 3.10 2.16
Entry Price 3.900 0.920
Delta 0.4682 -0.3235
Vega 0.1284 0.116
Prob OTM 0.5679 0.6407
Volume 130 32
Implied Vol 0.2791 0.2982
Gamma 0.0441 0.0373
Contracts 200 200
Close $610.00 $432.00
Open $780.00 $184.00
Profit/Loss -$170.00 $242.00

Macroaxis Financial Analytics provides a complete breakdown of relative analytical elements used by portfolio managers.   We use Macroaxis for several reasons; in this case we wanted to see what the overall market sentiment was for Adobe prior to their earnings release.  We’ll incorporate this feedback into making a case for leaning into either the Call or Put.

screenshot-www-macroaxis-com-2016-09-12-18-09-50

GRTS Market Analysis and Codexquant are meant to present educational thesis’ to provide insightful means of attaining impartial investing decisions.  The take away, moreover is an orientation to confront “our” bias and blind spots when it comes to the truth.  Traditional means are quickly being replaced by highly advanced technological devices and programs.  If we can provide one with a new insight of what’s valid in revising the way we assess our global economy, than we’ve achieved our purpose.

NBBO: THE DIRECT FEED RACE TO LIGHT SPEED EXECUTIONS

Forex Dealing Break Down (1)  Click on the link to you to open the “pdf” graphic of trade execution flow exchange comparisons between retail traders and High Frequency Trading algorithmic “machine-to-machine” trade executions.

How Slow is the NBBO?

A Comparison with Direct Exchange Feeds

the oracle machine”

Thirty percent of Wall Street’s intraday liquidity is transformed through price formation acceleration, explicitly influenced by high frequency trading thriving in millions of dollars of profits by exploiting the exchange’s trade execution “latency” electronically sent over the Internet ot capture the “best price” trade executions for the consumer trader.

To be efficiently profitable as a predator on incoming bid/ask trades, the HFT servers are set up is co-located server near an exchange/electronic communications network premise and plugged into a ultra-fast bandwidth. Through network stacking, messaging protocols and raw market data processing.

Though the financial media claims HFT brings greater “liquidity” to the market, in reality it is causing price dislocation for the consumer day trader. For example, the NP-Hard can be used as source code applied to the arbitrage decision problem presented by the variance of the bid and ask spread among the exchanges data flows. Here is where the “investors” and “brokers” can virtually integrate the exchanges through their computational technology without “transparency” to the consumer trader.

clock-rate – faster to market”

Likening the linear exchanges to a convex polyhedral geometric, the functional aspect of the HFT algorithm parameter compilers are encoded into proprietary computational complexity that solves the “problem” through polynomial time within the concrete semantics of an oracle machine or “black box”.

The HFT algorithms must be able to reconfigure frequently in a compute-intensive application to efficiently respond to public data order flows, thus HFT platforms have utilized custom-optimized “field programmable gate array” (FPGA) integrated circuits.

Contemporary (FPGA) optimize I/O speed and allow for programmable logic blocks to be wired together. This is key to minimizing data flow through the bandwidth to the exchange. Though time consuming to program these “logic blocks” can be configured to perform complex combination functions.

Moreover, as mentioned above, soft processor cores implemented with FPGA logic are equally robust. Achieving the ability to be re-programmable at “run time” is leading the FinTech pack with these HFT systems; including non-FPGA architectures (

ref: Redline Trading Sources).

Today, FPGAs are being replaced with an “off-the-shelf” processor (Stretch S5000).

Software-configurations based on “clock rate”, such as the Stretch S5000 are an adaptive hybrid of the FPGA. Having software that is configurable within the processor associated with the general-purpose processor (GPPs) and DSPs and application specific processors (ASPs), serving parallelism and flexibility with FPGAs, programmable logic that is completely embedded inside the processor architecture.

Moreover, the Stretch S5000’s patented Instruction Set Extension Fabric (ISEF) is a game changer in the world of being able to program a processor for compute-intensive applications. The sky is the limit now for HFT to overcome and exploit exchange trade executions clock time.

ping bugs”

The “ping” comes from active sonar terminology so named by Mike Muuss in 1983. It is the means of sending a pulse to a target host across the Internet Protocol (IP) network. In consideration of market exchanges, the “ping” represents a “price prob” activated by an application programming interface (API) plugged into a specific market exchange.

Consequently, by using the “ping” proprietary data feeds (expensive subscription access) have a tremendous advantage over “public” (consumer) consolidated data feeds in consideration of trade execution latency. Even though The National Best Bid and Offer (NBBO) is meant to “halt” price dislocations (latency), but it has shown otherwise.

In light of the highly advanced computational algorithm trading systems such as value weighted average price (VWAP) and weighted average price (TWAP), HFT algorithms maintain an informational advantage by remaining constantly plugged into the major exchanges (listed below) to use latency issues to their advantage.

It is no longer the case that the price shown upon trade execution will be the fill price. Maintaining NBBO “best price” is undermined even more are inter-market sweep orders (ISO) and non-transparent dark pools that send a trade execution to multiple exchanges for instantaneous execution, disregarding the “best price” regulation. This is allowed by the SEC.

13 exchanges and nowhere to go”

Wall Street has two trading systems. Registered exchanges and alternative trading systems. The exchanges are regulated to provide the “best price” through the consolidated quotation system (CQS), and must file any rule changes with the Security and Exchange Commission(SEC).

The electronic communication networks (ECNs) and dark pools, do not provide CQS, but are mandated to match NBBO price quotations. In 2007, the Securities and Exchange Commission established Regulation National Market System (Reg NMS) to protect consumer traders from improprieties of the “best price” execution.

Reg NMS requires the exchanges to provide the quotes to the primary exchanges, such as NYSE. Data that is collected under the Security Information Processors (SIPs) for the NYSE and NASDAQ publish the National Best Bid and Offer (NBBO). Consequently, brokerage houses are required by Reg NMS to give consumer traders the best price at execution.

Out of the 13 exchanges accessed for market trading, the NASDAQ, NYSE, NYSE ARCA, BATS BZX, Direct Edge EDGX and EDGA have approximately eighty-eight percent of the lion’s share in total volumes. Dark pool trades, that are non-transparent account for more than 12% of the trading volume.

 

the larger the latency, the greater the uncertainty”

For consumer traders, this short duration of dislocation of price, becomes costly in commissions while bolstering optimal profits for HFTs. Empirical data comparison from examination of publicly traded market data and data sold directly from exchanges (tapped by High Frequency Trading algorithms) proves the fact that the “latency” of the consumer’s executed trade is picked off by HFTs, monitoring the data flow with direct access to the exchanges.

In one control study between the public NBBO and a synthetic NBBO (Redline Trading Source using a software programmed processor similar to the Stretch S5000) turned up 54,734 price dislocations; tabulated within 6.5 hours of the trading session with the equity Apple (AAPL).

It is estimated that price dislocations happened every 2.34 seconds with the latency lasting as long as 1.5 milliseconds. Consumer trades went to the wrong market exchange 0.175% of the time. The average price dislocation was $0.034.

 

MEASURE YOUR COMPUTER’S TRADE EXECUTION LATENCY

http://www.dukascopy.com/fxcomm/fx-article-contest/?How-To-Measure-The-Latency=&action=read&id=948&language=en

The “packet-switched” network measured either “one-way” or “round trip” is being exploited as a “fixed game”.

 

AAPLE TAKES A DIVE

STATUS

 AAPL GOOGLE docs.google.com 2016-04-24 12-38-11Talking in a quick look at AAPL in relation to the S&P 500 shows a dramatic break away on the iconic tech leader, with earnings coming up.  It has been our bell weather asset for quite sometime and this graphic shows why.  Facebook (NASDAQ:FD) has taken the lead.  We expect FB to up around $250 by the end of the 2016.

CODEX QBT COMPUTATIONAL EQUATION INPUTS FOR EXCEL

CODEX.QBT – MATRIX
STATISTICAL PARAMETRIC 
EXCEL SEQUENCE EQUATIONS AND COMPUTATIONS

INPUT DATA MATRIX

Symbol

Impl Vol

%Change

Close

Open

AG

0.61

0.03

10.8

11.06

STRIKE CALL

INTRINSIC

C PROB OTM

C PREMIUM $

10.00

0.82

0.34

1.00

1.20

0.04

BIDU

46.39%

2.19%

170.14

172.38

STRIKE CALL

INTRINSIC

C PROB OTM

C PREMIUM $

175

5.11

0.56

8.2

INTRINSIC IS THE DIFFERENCE BETWEEN THE UNDERLYING AND STRIKE PRICE FOR CALL CALL OPTIONS;

PUT OPTIONS IS THE DIFFERENCE BETWEEN THE STRIKE PRICE AND THE UNDERLYING

TGT: =SUM(HIGH PRICE-LOW PRICE)+OPEN

TGT 2: =SUM(OPEN PRICE – CLOSE PRICE)+LAST PRICE

INTRINSIC VALUE IS THE ACTUAL VALUE BASED ON AN UNDERLYING PERCEPTION OF ITS TRUE VALUE, BOTH TANGIBLE AND INTANGIBLE.

TGT

TGT

RANGE

11.59

11.42

0.79

P PROB OTM

P PREMIUM $

IV POP

0.65

0.35

0.48

0.04

CALL IMPLIED VOLATILITY- PROBABILITY OF PROFIT

PUT IMPLIED VOLATILITY – PROBABILITY OF PROFIT

CALL IV POP: =SUM(STDEV IV – CLOSE)

PUT IV POP: = SUM(STDEV IV – PUT PREMIUM

C IV

C HV

OPT IV

STDEV IV

0.57

0.45

0.61

0.833

STANDARD DEVIATION EQUATION FOR IMPLIED VOLATILITY

=STDEV(C IV; C HV; OPT IV)*10

DELTA

THETA

GAMMA

VEGA

0.710

-0.009

0.230

0.010

-0.290

-0.009

0.032

0.088

CASH DELTA

= DELTA* UNDERLYING PRICE * POSITION SIZE

THETA = 10,000 * -1 = -100;

MEASURE OF TIME DECAY FOR A ONE DAY TIME HORIZON;

EXTRAPOLATED OUT TO EXPIRATION

PROFIT/LOSS

CASH DELTA * SPOT CHANGE IN %; (CASH GAMMA * SPOT CHANGE IN %)/2;

THETA*NUMBER OF DAYS (USUALLY 1 EXCEPT FOR W/E;

VEGA*CHANGE IN IV

Q & P WORLD

(QUANTITATIVE – OPTIONS; PORTFOLIO – EQUITIES)

P ACTION

1.55

PRICE ACTION IS ESSENTIAL IN COMPARISON TO NET CHANGE. IT ACTS AS THE LEADING INDICATOR FOR INTRADAY PRICE MOVEMENT DIRECTION/REVERSAL

=SUM((LAST PRICE-OPEN PRICE)+(LAST PRICE-HIGH PRICE)+(LAST PRICE-LOW PRICE))/1.8

NET/PRICE MOVEMENT RATIO

=SUM(NET CHANGE/PRICE ACTION)

HV

STDEV

IV

0.91

0.96

1.17

HV (HISTORICAL VOLATILITY) IS COMPARED TO THE PRICE RANGE STANDARD DEVIATION AND THE IMPLIED VOLATILITY THAT SHOWS IF THE ASSET’S PRICE MOVEMENT IS VOLATILE – MEANING THE OPTION PREMIUMS WILL BE HIGHLY ACTIVE

ALPHA

BETA

EXP RTNS

-0.42

0.31

0.08

ALPHA:

=SUM(RISK FREE RATE)+(EXP RETURN-BENCHMARK)*(STDEV RETURN/STDEV MARK)

BETA:

=SQRT(EXP RETURNS)/ABS(HV)

IF BETA IS “2” IT WILL BE EXPECTED TO SIGNIFICALLY OUTPERFORM IF MARKET IS GOING UP, AND SIGNIFICANTLY UNDERPERFORM IF MARKET IS GOING DOWN.

IF BETA IS “1” THEN ASSET AND MARKET WILL GENERATE SIMILAR RETURNS OVER TIME

EXP RETURNS: =STDEV(IV;HV)*SQRT(DAYS/252)

BENCH: =SUM(CLOSE PRICE;OPENPRICE;LAST PRICE; HIGH PRICE; LOW PRICE)/5*0.01

STDEV RETURNS: =STDEV(PRICE ACTION;IV)

STDEV MARK: =STDEV(STDEV;BENCH)

BENCH

STDEV RTNS

STDEV MARK

0.64

0.27

0.23

LIST FOR BLACK SCHOLES CALCULATION

RISK FREE

0.25

 

EXP MOVE

SD SQRT

1.87

0.77

EXP MOVE:

=LN(PIVOT PRICE)*0.45

SD SQRT:

=STDEV(OPEN PRICE;LAST PRICE)*SQRT(EXP MOVE)

LOG

EXP

0.88

2.42

NATURAL LOG:

=LN(EXP MOVE/SD SQRT)

EXPONENTIAL:

=EXP(LOG)

SKEW

DAILY %

0.56

0.45

SKEW:

=SKEW(HV;STDEV;IV)/EXPONENTIAL

ALTERNATIVE:

=SKEW(STDEV;SQRT SD;LOG;EXP)

DAILY %:

=SUM((PRICE ACTION)*0.1/SQRT(DAYS/252)

ADD: MEAN AND VARIANCE

PIVOT

64.01

PIVOT PRICE:

=AVERAGE(PRICE SERIES)

OR

=AVERAGE(OPEN;LAST;HIGH;LOW PRICES)

CHG %

1.01%

PIVOT PRICE:

=AVERAGE(PRICE SERIES)

OR

=AVERAGE(OPEN;LAST;HIGH;LOW PRICES)

CHG %

1.01%

=SUM(HIGH PRICE TGT-HIGH PRICE)*1/HIGH PRICE TGT

HIGH

66.07

=SUM(OPEN+EXPONENTIAL)

LOW

65.29

=SUM(HIGH PRICE TARGET-SD SQRT)

INT

EXT

#N/A

#N/A

INT: =SUM(LAST PRICE – STDEV)+IV

EXT: =SUM(LAST PRICE – INTRINSIC)

STDEV

#DIV/0!

=STDEV(CLOSE;OPEN;LAST PRICE SERIES)

SV

IV

VOL

#N/A

#N/A

#DIV/0!

STATISTICAL VOLATILITY

=AVEDEV(CLOSE;OPEN;LAST;HIGH;LOW)^0.314

TO FIND IMPLIED VOLATILITY RANK (INTRADAY)

=STDEV(OPEN, HIGH, LOW, LAST PRICE RANGE)

THEN,

=SQRT(STDEV)

A-B

#N/A

=SUM(ASK-BID)/VOLUME

RULE:

WHEN SV, IV, VOL ARE NEAR NEUTRAL THIS IS A BUY SIGNAL –

STDEV SIGNALS AN ANOMALY IF IT LOOKS LIKE AN OUTLIER TO THE TRIAD.

GROWTH

VALUE

SD SQRT

#DIV/0!

#DIV/0!

#DIV/0!

GROWTH:

=GROWTH(LAST PRICE; HIGH PRICE;SV;IV;MIN+TIME VAUE)+MIN PRICE TGT

VALUE:

=SUM(GROWTH-LAST PRICE)

SD SQRT:

=STDEV(IV;VOL)*SQRT(DAYS/252)

ALTERNATE:

=STDEV(ROR;ROC)*SQRT(DAYS/252)

=STDEV(HIGH PRICE;LOW PRICE)*SQRT(DAYS/252)

MIN + TV

#N/A

TIME VALUE

=SUM(MIN+EXTRINSIC)

INDEX

WEIGHT

#N/A

#DIV/0!

INDEX:

=AVEDEV(PRICE SERIES)/5

WEIGHT:

=SUMPRODUCT(STDEV;SV;IV)*PRICE ACTION

ROR

ROC

#N/A

#DIV/0!

ROR:

=SUM(HIGH PRICE-LOW PRICE)/ABS(HIGH)

ROC:

=STDEV(HIGH;LOW)*SQRT(DAYS/252)

MEAN

#N/A

MEAN

=MEDIAN(OPEN PRICE;LAST PRICE;HIGH PRICE;LOW PRICE)

P TGT

EXP MOVE

#N/A

#N/A

PRICE TARGET:

=SUM(LAST+PRICE ACTION)

EXPONENTIAL MOVE:

=(PRICE INDEX)*45/252)

CAN VARY TIME FRAME USING 1/16TH FRACTIONAL

VAR

KURT

0

#DIV/0!

VARIANCE:

=VARP(OPENPRICE TO LOW PRICE SERIES)

KURTOSIS:

=KURT(SV;IV;ROC;STDEV)

ALPHA

BETA

RSQ

#N/A

#DIV/0!

#N/A

ALPHA:

=SUM(IV)+(PRICE ACTION – STDEV)*(INDEX/WEIGHT)

BETA:

=FTEST(IV;NET CHANGE; PRICE INDEX)

PEARSON:

=PEARSON(HIGH, LOW, CURRENT PRICE; NET CHANGE, PRICE ACTION, PRICE INDEX)

(Disclaimer:  The data presented is intentionally provided for educational purposes only.  We are not making any recommendations nor implying that this statistical set up is a proven format for making profitable trades.  Our intent is to expose traders to the combinations of scenarios regarding Excel equation inputs and the means of calibration by making a modular layout of specific “family” statistical inputs that can be supportive for other modular bins.)

Peace.

Rich

CODING THE EXCEL SPREADSHEET – STRANGLES AND STRADDLES

Our new model brand logo is <codex.qbt> of which you’re going to see more of in the future as our signature icon.

It is baseline defined as a “code” for programming of which eventually moves from binary code toward a “Qubit” superposition quantum program language.

We are using Excel as a baseline platform in determining Proof of Concept of parameters inputs correlated to equations and networking calibrations.

We have posted our latest video showing the simple sequencing of data input from the Thinkorswim (TOS) platform into Excel for the Strangles and Straddles template.

Click here to view the video on setting up our options template spreadsheet.

When it comes to trading “Strangles” or “Straddle” spreads, there few quantifiable “optics” that show a comparison of movement between the Call and Put premiums, calibrated to your “entry price” to give you enough insight to entrain your cognitive decision making towards more profitability.

Nor are there calibrated Excel spreadsheets that give you flexibility with defined ranges of which you can “code” yourself though the Open Source format.

We developed this spreadsheet template for traders to use in simulating their thesis and/or in executing actual trades to track the profit/loss ratio.

It is meant for the novice to have access to an introductory process, so complexity is minimized.

The optics reflect to the user just exactly how the choice of strike price is affected comparatively to the underlying assets price move, volatility and option chain month choice.

There are so many scenarios with option spreads that it takes time to learn which strategy works best under specific market performance conditions.

We are all guilty in trading losses through our stubborn “confirmation bias”‘ so these templates are meant to be “impartial judges” to confront bad default habits and entrain your subconscious mental decision making processes toward refined risk defined knowledge that triggers profitable executions.

The <codex.qbt> Excel templates shown in the videos are available per request, and for now, gratis.

Please give me some time for turn around as I’m the only one managing this the plethora of requests.

Peace.

Rich

VXX LONG CALL FOR A BEARISH WEEK

The stats are in our VXX trade, entered 14 days ago – closed on Friday 13, 2015.

Stock quote and option quote for VXX on 10/19/15 10:11:06

DOW S&P 500 NASDAQ RUS 2000 VXX
-194.010 -21.600 -52.978 -7.545 0.969
-385.90 -42.86 -113.31 -18.69 0.0681
-1.16% -1.12% -1.54% -0.72% 1.29%

VXX – UNDERLYING PRICE STATS AND COVERED RETURN/INVERSE FORMULA

Look on the Right side column “C-Return” and “Inverse”.  This is the key factor in knowing whether to lean into the Call or Put side.

Asset Price 21.96 C-Return 1.4900
Net Change 1.400 Inverse -0.4900
Price Action 1.9100 Inverse P Act 0.7236
STDEV Price 0.361 IV Rank 0.6005

Below is our “front month” outcome – November Option Cycle.  The Call reaped $1779.00 in profits, while the Put fell prey to “time decay” eventually zeroing out almost all Ask/Bid strike premiums.  Our loss was $0.38.

NOV 15 (14) 100

Call OFFSET Put
$1,179.00 $1,179.38 $0.38
Strike 17 Strike 18
Premium 5.05 DELTA Premium 0.040
Entry Price 1.12 PUT Entry Price 0.039
Target 5.19 Target 0.18
Skew Price 5.21 IV Skew Price 0.206
Delta Hedge 0.2430 CALL Delta Hedge 0.811
Gamma 3.5006 Gamma 0.1106
IV 0.610 SPRD > .0003 IV 0.417
Intrinsic 3.900 CALL Intrinsic 0.015
Strike 17.00 ~ Strike 18.00
Ask-Bid Spread 0.000 PUT A- B SPREAD 0.000
Prob of Profit 0.2419 ~ Prob of Profit 0.800
Profit/Loss $1,179.00 Profit/Loss $0.38

(Disclaimer:  This is posted for educational purposes only.  We make no claims or advise to make trades based on this data.  All of our posts are Proof of Concept for a working Excel spreadsheet model titled <codex.qbt>.)

For more information please write to use at: tecktomaket4@gmail.com immediately.  You don’t want to miss out the insightful data inputs that help your trading strategies to be mechanical and profitable.

“QUIRK MODE”: INVENTING EXCEL QUBIT GRAMMAR

Without going into great detail and laborious contextual arguments that comprises a thesis or worse yet a “white paper” at this time, here is the initial visual introduction of our revamping of Excel equations and formulas we’ve been using for robust profitable investing.

The sole purpose of this project is to push encryption code through keyword behaviors that tested and evaluated by description so as to prevent deprecation hacking; going beyond syntactical constraints such as the XML schema.

Moreover, we express a limited capability with data type syntax such as add/edit, through “PUBLIC” Formal Public Identifiers (FPI), to be used for global financial market’s routing exchanges ISP data packets.

By using Boolean predicates, the data content must satisfy the governing context ( say between the exchanges Bid and Ask spreads) filtered through specialized rules that comply with “uniqueness” and “referential integrity”.

QUANTUM COMPUTERS – D WAVE – QUBIT

At the core of our revision is incorporation of the Qubit – or quantum bit – that is fast becoming known through the acceleration of the D Wave quantum computer, and its quickly evolving incorporation into our global social network, given a recent combined purchase by Google and NASA for $10 billion dollars.

Dominance of our global networking economy through internal means, is a mechanism that can override just about every programming language used today.

Qubit superposition algorithm driven trading, i.e. High Frequency Trading, is quickly morphing into augmented intelligence, the interplay between human and machine; the later taking on a more more dominate role.  The constructive aspects of this can be tackled in many formats, all of which have the same means to the end: profitability or worse yet, skewed presentation of profitability.

The challenge to us is if we can dial down this high level of sophisticated black box engineering to the most common denominator of a stylesheet language to be juxtaposed into the “backward compatibility” or  “quirk mode”  using Excel grammar and/or a similar mathematical formatted spreadsheet.

The instruction provided by the coded syntax is associated with document type definition (DTD), where the mark up conforms to the layout mode of quirk mode rather than “standard mode”.

We change the “text/html” serialization of HTML 5, for example, (not SGML-based), to “qbt/html”.

The basic HTML parsers that don’t implement full SGML-based documents such as HTML, become associated with a notable system identifier, the cataloging file resolving the FPI system identifier.

We hypothesize that since the “qubit” superposition control through magnetic gravity – using a phosphorous molecule as the directional compass for in deciding its choice between the superposition binary coded one or zero; we seek compatibility, through the use of Excel as a baseline platform, coupled with a “real world” scenario – Wall Street for validation of our implementation of this rendering mode.

We’re in development of implementing a “Qubit’ stylesheet, that involves highly innovative coding that is not traditional nor is presently used.  Consequently, as much as it sounds to be an experiment in futility, it can attain Proof of Concept if we approach the over-all architecture in a serialized format that takes into account HTML, XML, and

Moreover, we compartmentalize input/equation “bins” that have specific functions that can be seamlessly calibrated with a high degree of confidence to trigger the presence of a “Document Type Declaration” or DOCTYPE, for example.

We are piecing this together having built over 100 Excel templates.  It has been painfully difficult through trial and error over the years, though we remain motivated as much as the first “ah ha” moment of inspiration we experienced in 2012, and even going further back to 2000 when we delved into Quantum Mechanics analytics in trading Forex through Dr. Duka’s Dukascopy Forex trading platform located in Geneva, Switzerland.

EXAMPLES – PRICE FORMATION CHARTS

price charts

Market Performance – Dow, S&P 500, NASDAQ, Russel 2000

Using only the Net Change in comparison to Price Action

aapl price

Apple (NASDAQ: AAPL)

Price Range: Open, Bid, Intraday, Ask, Last

The positioning of the “Last” price in relationship to the High, Intraday, and Low prices gives us an immediate optic on the price direction – bullish, bearish or range bound.

Trend Line Polynomial – 2 Periods (Includes R-Square)

msft price formation

Microsoft (NASDAQ: MSFT)

The same parameters used with AAPL.

vxx price formation

IPath SP 500 VIX Short Term FUT ETN: VXX

Incorporating the futures index gives us a more robust insight to a possible “mean reversion” during trading hours.

There are a total of 12 charts calibrated to our Excel spreadsheet.  The entities are selected from our watch list data; a cache of forty-seven assets that are notable for both Implied Volatility, Dividend payouts, and a combination of “growth” and “value” entities robustness.

The Codex .qbt Excel Chart relates to the end-user (trader) real time price movement, based on five price range elements for “real time” price tracking through “Thinkorswim” (TOS) platform.  Latency is approximately 900 -3000 milliseconds using a conventional desktop PC and Ethernet cable connection.

Our input parameters are the current price range; Yesterday Close, Open, High, Low, Last, Bid and Ask.

POLYNOMIAL TREND LINE

A polynomial trend line set at 2 periods (Green line with arrow) to provide us with a sense of the prevailing directional move and any anomalies, e.g. “shock event” or “HFT Shark Bait” inventory spike.

 

We are working on presenting this “live” and/or available for traders using TOS.

Contact us if you’d like to receive a template: grtsmarket@gmail.com

(Disclaimer:  This post is for educational purposes only.  We make no claims or provide information for making trades or investments.  All of the copy presented is for simulation and hypothetical scenarios and is the sole property of GRTSMARKET.  Reproduction of this material is available per prior request through: grtsmarket@gmail.com)

 

THINK BACK: TWITTER EARNINGS – THE WHIPSAW OPTION STRANGLE TRADE

In this blog we discuss how we found which option side to lean into prior to Twitter’s (NASDAQ:TWTR) earnings release the following day – 10/27/2015.

It never amazes me when the predictable is always the unpredictable when it comes to trading options around earnings.  The “zero sum” outcome and randomness of the efficient market indicators are equally reliable to be unreliable.  What goes down must come up.

And paradoxically, TWTR posted a plus in earnings, though minuscule by our standards, market makers obviously have inventory lined up to short TWTR, proving that our PUT weighed values were correct: but only momentarily since TWTR returned to the breakout price on during intraday trading on 10/28.

Posted Table on October 26, 2015 the day before Twitter posted earnings.

Estimate is -0.24 cents and actual was 0.10 in After Market Reporting.  What transpired as to the volatility of movement provides excellent feedback as to why you’re flying blind, even with statistical evidence to support a “mechanical” risk defined trade.

In this table below you’ll see our goal post statistical data indicator’s comparison between the NOV 2015 Call/Put analytic matrix that is calculated by our Codex.qbt* formula.

How to read this graph:  On both left and right sides we have Call and Put coinciding indicators: Premium (Ask), our entry price (calculated for a limit order entry), Bio premium price, Delta Hedge (not direct Delta, but our own calibration), Gamma, Implied Volatility at the Strike, Probability Out-of-the-Money, Strike, Number of contracts in hundredths, Probability of Profit (calibrated to our own formulary), P/L, and Skew price.  Note: Intrinsic is purposely left blank as this is a short term trade.

In the middle are the Logic signals:  Delta Hedge shows “Call” and Implied Volatility shows “Put”, a contradiction that played out accordingly.  The “Spread .03” Alert is to signal us when the spread between the Bid and Ask expands greater than two cents.

Stock quote and option quote for TWTR on 10/26/15 08:13:03
CALL P/L Strangle PUT P/L
$38.00 $260.00 $222.00
NOV 15 (25) 100
CALL OFFSET** PUT
Premium 1.98 $260.00 Premium 2.060
Entry Price 1.78 DELTA Entry Price 1.931
Bid Price 1.93 PUT Bid Price 2.07
Delta Hedge 0.3381 Delta Hedge 0.427
Gamma 0.0887 IV Gamma 0.1272
IV 0.743 PUT IV 0.760
Prob OTM 0.61 Prob OTM 0.52
Intrinsic Spread > .03 Intrinsic
Strike 32.00 CALL Strike 30.00
Contracts 100.00 ALERT Contracts 300.000
Prob of Profit 0.1921 PUT Prob of Profit 0.1935
Profit/Loss $38.00 ALERT Profit/Loss $222.000
Skew Price 1.94 Skew Price 2.209

What sticks out is that the Implied Volatility is .743, well above our .45 threshold for mean reversion of a Buy Call signal; and that the Put indicators out weigh the Calls.

With a favorable out come of TWTR going short, we executed a cost reduction limit order with 3 Long Put contracts and 1 Long Call contract, to protect the upside, so not to diminish our profits.  You’ll notice we were pretty close in matching up the premiums for pairwise management of risk to reward ratios.

twtr earnings
Entered around $30.89. Surged up for a Call scalp profit and closed out at closing bell. Held the Put side to the opening bell on 10/28 to lock in profits at $28.80.

TOS “think back chart” (The outcomes are not as accurate given the intraday subtleties involved in our trade executions.)

The totals are listed in the above graph: Call profit was $38 on one contract and Put profit was $222 giving us a $260 pay out minus the per contract fees.  The whipsaw volatility on price formation was foretold by the OTM percentage.

*Codex.qbt is our bootstrapped Excel quantitative statistical model name.

It is in the Proof of Concept phase; based on the hypothetical Qubit “superposition” of categorical data inputs compiled into statistical data bins then calibrated for a “measure of certainty” outcome.

**Offset is the combined profit/loss between the Call and Put positions.  This is an excellent visual for “Strangles” and “Straddles” as you can see how both positions can become profitable at the same time.

Do you use TOS?  Would you like to have our Open Source Excel spreadsheet models for your own use?

Available for MSN Excel 2010, Apache Open Office and Google Spreadsheets.  These spreadsheets can be customized for your own trading style and watch list selections.

Inquire at: grtsmarket@gmail.com for a list.

Peace.

RK

Disclaimer:  The above information is for educational purposes only.  We make no claims of validity or suggestion for trading the assets listed.

WRITE HIM OFF – Z EQUALS ZERO

Quant Org Internal Memo – received July 5 by Def-Vet from E.M

Author Code Name: Woet.  True Identity unknown at this time.  Considered high security risk for the national financial sector.

“Write Him Off” – Zine Equals A Zero

The new updated report on the probability calibration of trouble shooting the Zine algorithm carried a lot of problems with the probability density function that surfaced as an unknown and difficult to understand .

I surmised this because of the notation bugs resulting from the raw data qubits in correlation to the combinations and permutations of binary data mining inputs which upon the advent of a forced upgrade gave only the instructions for “x” that I found written on the wall in the office kitchen by the firm’s trustee who is often slow to be glad about my current position as data analyst, because he is a die-hard traditional mind-set of frameworking the tiling isohedras gathered from the TOS platform.

The less the limbs on the decision tree, I claimed that everything comes in threes pertaining to the measures of the percentiles and quartiles, ref: Ian Steen. He claims the recent score for in-house or not is his only standard data into one hot flipflop of the swithces that equates Clarks – 40 S Saint Theorem based on Laurent linear polynomial format.

A recent thesis by Rees, et al shows that they denied this by at least one P separation, ref. Seth E ETC; when you start at 90, he said that Warren’s data is the first percentile of the city’s demographic while I doubt his algorithm session performed without a flaw from the “glide” which is suppose to present no doubts that the Notation is an alternate code alone at best to intuit the percentile construct built into the adaptive agent learning memory.

It was expressed at the board meeting that with the firms aggregate years of experience, that should enhance the unit of intelligence to a 10/80 power, our ability to supersede humanity’s own ability to absorb experiences at 650 milliseconds – deviated by 3 milliseconds per neural synapse, should be 15% greater than the citizen consumer based on a control group of 91 witnesses to the machine-to-machine prototype.

This extends Tesselers equal or better than 75% social class needs to notice that we’d been already prepared infinitely per person to collect substantial profits from what was clearly 45% of the “witnesses” bias performance valuation versus the “child-parent union” factor.

Consumer response to the product launch was 20% higher than expectations while in the second quartile analysis, the thought processing averaged per millisecond or glide was 20% higher confirming that going forward will be just routine for the third programming quartile session. The citizen’s techno-device quest was identified by 4% use of the quarter range of our own data set; based on global consumption and natural resource availability.

Cheating bias was in need for the entire data set to be firmly set with empirical evidence in the first quartile correlated to the standard score median that was already shooting beyond the third quartile score.

This indeed helped me to own what was Kingsley’s proof of concept prior to my own thesis release, showing that the man lives on an island when it comes to incorporating the irrational user behavior numbers between what is displayed here as step one, and arranged accordingly through the sequencing.

We’re in small ways moving towards the largest break through in quibit coding, but facing less outside funding which means that our entire data set goal may or may not have an even chance of meeting the valuation deadline.

The only means of accomplishment at this time is to find a deep pockets cell among the 19 Seventies Equity Funds that she and myself are tuning into for an equal split on top of the 35 plus 45 ratio, designated by the late 19 – 40 baseline point system that is worth taking into consideration given our game changing breakthrough with fungi circuitry for the motherboard if we are given patent approval, re: 1223 in the 35 segmentations of required purposes.

To be sure we remain ahead of our competition, I added the all-odd numbers scores squared by integers that result in the “bringing in the money” outcome. No valued headlines in the media regarding a possible competitor or infiltrator to our project.

This is a relief since Switches and Witches reported that the median of old values are heating up certain sectors, three in fact reporting a code sequenced output of 35566778 to the power of 79 – known as the male bonding citizen element in human DNA. The TOS quartiles define this appropriately as the termed “injured portal ranges” that I discovered last Saturday after receiving a text from outlier user E. Martinez (code name DG 3-2). He reported that one 67-12 equaled 51 points in the set of data which minus “King One” closes the critical logic gate that must produce many standard deviations by perceptive observation that is fundamentally a responsibility of in our Eastern research court.

They calculated the following formation based on the quad-triangle analysis of the Zuni temple. Digging through my archival notes of hundreds of failed arguments in the algorithm Zine, all produced by “halt” calculations showed that Team Score X didn’t do the observations that I had instructed them to do, which made me have to present my ITM in front of the firm’s CEO.

But in terms of the interchangeability of less content, as I explained, for the standard score, I actually provided the proof of concept result without the Asian sector’s trouble shooting support saving our firm millions of dollars. Regardless, the mean score came out at 85, equivalent of the test score from the formula I created known as “Three Easy Zero”.

In this formula “E” equals zero, that makes the program quirky enough which was inspired by my niece Courtney’s own quirky behavior in decision making; who is a complete deviation of zeros from sigma.

Anyway I resolved the upper echelon’s Zoo Courts dilemma by declaring that their inputs into the formula were “walls” and to get around this was to calibrate the sub-syntax code to five minus the new standard deviation embedded in Zine – line 3298756-123-a234.

That reduces the outcome to exactly where we wanted the prototype to be expressed for confirmation of being bug free – proved by the seeing on the computer monitor: “Write Him Off; Z Equals Zero”

9AM MARKET UPDATE – MARKET INDEXES SLOW TO REBOUND

May 7, 2015

Here’s our 9 AM (PST) equity matrix update for intraday trading.  Price Skew alignment verifies the Price Target.

Ticker Pearson Last Quote Forecast Target Price Skew
AAL 0.3635 49.24 49.59 50.68 50.68
YHOO 0.8051 43.61 43.93 43.44 43.44
GOOG -0.0315 531.2 533.26 535.93 535.93
VXX 0.9302 21.8 21.75 21.65 21.65
IBM 0.6700 171.54 171.86 172.97 172.97
UPS 0.3645 99.72 99.76 100.00 100.00
TWC 0.1592 156.3 156.63 157.10 157.10
HFC 0.3391 41.11 41.40 42.20 42.20
BLUE 0.2177 150.15 151.82 156.31 156.31
HD 0.4630 110.21 110.55 111.62 111.62
MSFT 0.4027 47.04 47.20 47.62 47.62

LOGIC MATRIX – VOLATILITY PROBABILITY 

TICKER IV>SV IV>STDEV Mean>Last
AAL SELL SELL BUY
YHOO SELL SELL BUY
GOOG SELL SELL BUY
VXX BUY BUY SELL
IBM SELL SELL BUY
UPS SELL SELL BUY
TWC SELL SELL BUY
HFC SELL SELL BUY
BLUE SELL SELL BUY
HD SELL SELL BUY
MSFT SELL SELL BUY

 

Microsoft (NASDAQ:MSFT)continues to inch upwards.  Here is the Pearson graph posted on Macroaxis.

msft pearson correlation coefficient

 

Disclaimer: This post is for educational purposes only and not intended to give trading advice or recommendations.  The charts are provided as a courtesy from Maxcroaxis, a financial services website for portfolio analysis and optimization.