In this hour-long session, Steve Hill, CEO of AIQ Systems, will Unlock Technical Patterns Using Fibonacci Arcs, Time Zones, Retracements, and Fans and identify key support and resistance levels, predict potential reversal points, and plan entry and exit strategies.
January 16, 2025 5 – 6 pm Eastern
Register at https://us02web.zoom.us/meeting/register/xkZpkensQFS5P2wfOOH5CA
An hour-long session with Steve Hill, CEO of AIQ Systems. An hour-long session with Steve Hill, CEO of AIQ Systems. With this session, Steve will delve into the nuances of Head and Shoulders (and Inverse), Triple Tops and Bottoms, Flags, and Pennants, Ascending and Descending Triangles, Measured Moves, Rare Patterns: Diamond Tops and Bottoms, Cup and Handle.
Download the Accompanying PowerPoint presentation.
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In this hour-long session, Steve Hill, CEO of AIQ Systems will trace the evolution of AI in trading from early expert systems through
The Early Days: Expert Systems, The Rise of Algorithmic Trading, the Introduction of Neural Networks in Trading, The Advent of Machine Learning and Deep Learning, the Role of Human Traders and Oversight, Modern AI Trading Systems and Tools, and Risks and Future Directions in AI Trading.
Current AIQ Market Analysis will also be covered.
Date & Time Dec 12, 202 4 5 – 6 pm Eastern
Register Now https://us02web.zoom.us/meeting/register/tZEkd-CtqzsqGNbwpQ182EW_FnCD63HYY3JF
The importable AIQ EDS file based on Neil Jon Harrington’s article in the October 2024 issue of Technical Analysis of Stocks & Commodities magazine, titled “Revisualizing The ADX Oscillator,” Introduces a new, re-envisioned ADX that provides more information to the trader and makes ADX readings more intuitive and easier to visualize, can be obtained on request via rdencpa@gmail.com. The code is also shown here:
!DMI & ADX indicators
!Coded by Rich Denning, 10/14/24
Dailyrange is [high]-[low].
Ycloseh is abs(val([close],1)-[low]).
Yclosel is abs(val([close],1)-[high]).
Trange1 is Max(Dailyrange,Ycloseh).
Trange is Max(Trange1,Yclosel).
define HarringtonLength 10.
define sLen 5.
define ADXlow 15.
define ADXHigh 40.
!To convert Wilder averaging to exponential averaging use this formula:
!ExponentialPeriods = 2 * WilderPeriod - 1.
days is 2 * HarringtonLength - 1.
!+DM CODE:
yhigh is val([high],1).
ylow is val([low],1).
rhigh is ([high]-yhigh).
rlow is (ylow-[low]).
DMplus is iff(rhigh > 0 and rhigh > rlow, rhigh, 0).
DMminus is iff(rlow > 0 and rlow >= rhigh, rlow, 0).
AvgPlusDM is expAvg(DMplus,days).
AvgMinusDM is expavg(DMminus,days).
!AVERAGE TRUE RANGE:
ATR is expAvg(Trange,Days).
!DMI CODE:
PlusDMI is (AvgPlusDM/ATR)*100.
sPlusDMI is simpleavg(PlusDMI,sLen).
MinusDMI is AvgMinusDM/ATR*100.
sMinusDMI is simpleavg(MinusDMI,sLen).
!PLOT sDIdiff as historigram:
sDIdiff is sPlusDMI-sMinusDMI.
ZERO if sPlusDMI = 0 and sMinusDMI = 0.
sDIsum is sPlusDMI+sMinusDMI.
sDX is iff(ZERO,100,abs(sDIdiff)/sDIsum*100).
!PLOT ADX as single line indicator with support at 15:
sADX is ExpAvg(sDX,days).
Green if sDIdiff > 0 and sADX > ADXlow.
Red if sDIdiff <= 0 and sADX <= ADXlow.
Code for the author’s indicator is set up in the AIQ EDS code file. Figure 7 demonstrates the indicator on a daily chart of the QQQ. Note that I did not implement here all of the colors the author uses. I implemented the strong up (green), strong down (red) and neutral (yellow).
FIGURE 7: AIQ. The indicator is displayed on a chart of the NASDAQ-100 ETF (QQQ).
—Richard Denning
rdencpa@gmail.com
for AIQ Systems
AI in trading, has evolved from early rule-based systems AIQ, to the advanced machine-learning tools we see today. In the early days, AI’s role in financial trading was centered on mimicking human decision-making through structured systems that applied pre-defined rules. These rules were based on standardized interpretations of technical indicators, such as crossovers and divergences, which traders frequently use during technical analysis.
Unlike humans who might analyze these indicators in isolation, early expert systems combined them to generate a comprehensive view, providing weighted insights and reducing manual effort. Over the past 40-45 years, AI has advanced significantly in speed, precision, decision-making, and reducing human error. While initial explorations in neural networks showed promise, the focus shifted to rule-based systems, which laid the foundation for today’s machine learning and deep learning models. These modern systems are vastly more sophisticated, but their roots in expert systems demonstrate the steady progression of AI in finance.
However, rule-based systems have limitations. They depend on predefined rules and lack adaptability. While this ensures consistency, the systems do not learn or adjust as market conditions evolve. This raises questions about how much markets change versus what is already accounted for in technical indicators.
The rise of algorithmic trading brought advancements, with firms like Renaissance Technologies leveraging statistical models and high-speed trading techniques, including moving averages arbitrage. These developments mark a significant shift from the rule-based systems of the past to the dynamic, high-speed tools of today.
This session also covers a current market update using the AIQ market Timing tools.