v2v dynamic trading system

v2v dynamic trading system

>>> ※ Pivot Fibs Plus ※

1. FIBS ON/OFF session button retention
2. Updated template

>>> ※ News-Events v1.81 ※

1. Fixed vertical Newsline
2. Updated template

This trading tool is equipped with Forex Factory's Newsfeed calendar data which made for any custom-built third-party Metatrader indicator. Now, with this tool, it is fused and modified with navigational buttons that trigger such a uniquely designed impact prioritization algorithm. For instance, an event type with currency filter selection is instantly filtered or highlighted based on news impact classification with just a click of a button. It also includes an on-chart event vertical line guides. Then lastly, an additional Newsfeed instant switching action from scheduled economic news data to another popular Forex Newsfeed source... such as DailyFX.com & ForexLive.com.

Get it here >>> Post #1

...just updated:

1. Code optimization only for some indicators
2. HA-APB indicator ─ added switch button session-retention of a selected candle type (e.g. Jap, HA, APB-HA)
3. Set default candle bar as Japanese candle-stick.
4. Updated template

>>> v2v dynamic trading system
>>> News EVent v1.81

※ Neural Network ─ HMA-DSMA ...v1.09

☛ Using a variation of HMA algorithm of low-lag to zero-lag, and then... fused with the following:
↓
☛ Jurik Filters/Smoothing and custom MA types
☛ Combined with Deviation-Scaled Moving Average Algorithm
☛ Better & Best Formula (Better & APB calculation)
↓
✦ The button text shows the average predicted open price
✦ As always this type of indicator eats a lot of CPU resources...

Brief theory of Neural Networks:

The Neural network is an adjustable model of outputs as functions of inputs. It consists of several layers:

1. input layer, which consists of input data
2. hidden layer, which consists of processing nodes called neurons
3. output layer, which consists of one or several neurons, whose outputs are the network outputs.

All nodes of adjacent layers are interconnected. These connections are called synapses. Every synapse has an assigned scaling coefficient, by which the data propagated through the synapse is multiplied. These scaling coefficient are called weights (w[i][j][k]). In a Feed-Forward Neural Network (FFNN) the data is propagated from inputs to the outputs. Here is an example of FFNN with one input layer, one output layer, and two hidden layers:

The topology of a FFNN is often abbreviated as follows: <# of inputs> - <# of neurons in the first hidden layer> - <# of neurons in the second hidden layer> -...- <# of outputs>. The above network can be referred to as a 4-3-3-1 network.
The data is processed by neurons in two steps, correspondingly shown within the circle by a summation sign and a step sign:

1. All inputs are multiplied by the associated weights and summed
2. The resulting sums are processed by the neuron's activation function, whose output is the neuron output.

It is the neuron's activation function that gives non-linearity to the neural network model. Without it, there is no reason to have hidden layers, and the neural network becomes a linear auto-regressive (AR) model.

─ As always this type of indicator with example settings below is a CPU resource hugger

Get it here >>> Post #1

Don't simply use a Moving Average on your chart... Let's make it a little bit complicated ; )─
For that, here is the ※ Long & Short Deviation Scaled Moving Averages... (DSMA) ※ ; )─ Enjoy and have a nice weekend!

DSMA was described in the “The Deviation-Scaled Moving Average.” article of July 2018 TASC Magazine There John Ehlers describes not only the Deviation scaled moving average but a cross of two DSMAs. ─ But with this indicator, it is a system multiple DSMA crossovers.

Similar to GUPPY indicator in terms of Moving Average values and how it is being used ─But with the following fused algorithms...

☛ JMA (Jurik Moving Average): Smoothing/Filter

─ Long DSMA acts like the Slow JMA, and

─ Short DSMA acts like the Fast JMA

☛ Hull Moving Average non-lag variation

✔ The DSMA is a data smoothing technique that acts as an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.

✔ Jurik Filters, Phase, and Smoothing
This indicator utilized JMA's (Jurik Research Moving Average) phase and smoothing calculation. Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates these complications forever and gives you the best of both worlds: low lag and smooth lines.

Ideally, for instance, a filtered signal may both be smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically results in lower profits. In other words, latecomers get what's left on the table after the feast has already begun. The JMA's improved timing and smoothness will astound you.

JMA is a powerful adaptive tracker that can smooth time series data with a very small lag, no overshoots and no oscillations. The algorithm is stable and avoids the complexities of neural networks. JMA delivers the best all-around performance for smoothness, accuracy, and timeliness.

✔ Hull Moving Average
There are many types of moving averages, the most basic being the Simple Moving Average (SMA). Of all the moving averages the SMA lags price the most. The Exponential and Weighted Moving Averages were developed to address this lag by placing more emphasis on more recent data. The Hull Moving Average (HMA), developed by Allan Hull, is an extremely fast and smooth moving average. In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time.

Get it here >>> Post #1

...v2v dynamic trading system is powered by JMA

And here's what the Jurik Research Team said about their algorithms:

What is the Theory Behind JMA ?

PART 1 : PRICE GAPS
Smoothing time series data, such as daily stock prices, in order to remove unwanted noise will inevitably produce a graph (indicator) that moves slower than the original time series. This "slowness" will cause the plot to lag somewhat behind the original series. For example, a 31 day simple moving average will lag the price time series by 15 days.
Lag is very undesirable because a trading system using that information will have its trading delayed. Late trades can many times be worse than no trades at all, as you might buy or sell on the wrong side of the market's cycle. Consequently, many attempts were made to minimize lag, each with their own failings.
Conquering lag while making no simplifying assumptions (e.g., that data consists of superimposed cycles, daily price changes having a Gaussian distribution, all prices are equally important, etc.) is not a trivial task. In the end, JMA had to based on the same technology the military uses to track moving objects in the air using nothing more than their noisy radar. JMA sees the price time series as a noisy image of a moving target (the underlying smooth price) and tries to estimate the location of the real target (smooth price). The proprietary mathematics is modified to take into consideration the special properties of a financial time series.
The result is a silky smooth curve that makes no assumptions about the data having any cyclic components whatsoever. Consequently JMA can turn "on a dime" if the market (moving target) decides to turn direction or gap up/down by any amount. No price gap is too large.

PART 2 : EVERYTHING ELSE
After several years of research, we Jurik Research determined that the perfect noise reduction filter for financial data has the following requirements:

1. Minimum lag between signal and price, otherwise trade triggers come late.
2. Minimum overshoot, otherwise signal produces false price levels.
3. Minimum undershoot, otherwise time is lost waiting for convergence after price gaps.
4. Maximum smoothness, except at the moment when price gaps to a new level.

When measured up to these four requirements, all popular filters (except JMA) perform poorly. Here is a summary of the more popular filters...

1. Weighted Moving Average -- not responsive to gaps
2. Exponential Moving Average -- excessive undershoot; noisy
3. Adaptive Moving Averages -- (not ours) typically based on oversimplified assumptions about market activity; easily fooled
4. Regression Line -- not responsive to gaps; excessive overshoot
5. FFT filters -- easily distorted by non-Gaussian noise in data; window is typically too small to accurately determine true cycles.
6. FIR filters -- has lag known as "group delay". No way around it unless you want to cut some corners. See "Band-Pass" filters.
7. Band-Pass filters -- no lag only at center of frequency band; tends to oscillate and overshoot actual prices.
8. Maximum Entropy filters -- easily distorted by non-Gaussian noise in data; window is typically too small to accurately determine true cycles.
9. Polynomial Filters -- not responsive to gaps; excessive overshoot

In contrast, JMA integrates information theory and adaptive non-linear filtering in a unique way. By combining an assessment of the information content in a time series with the power of adaptive nonlinear transformation, the result pushes the theoretical "envelope" on financial time series filtering almost as far as it can go. Any more and we'd be up against Heisenburg's Uncertainty Principle (something no one has overcome, or ever will).

Updates... for v2v dynamic trading system

1. HA-APB
   1. Removed classic PAC option
   2. Fixed button session retention and default candle type selection
   3. Code optimization
      

2. Neural Network - HMA-DSMA-Jurik

    

   1. By default, it is now set to run on volatility triggers by Volume on Main Chart indicator
   2. Code optimization
      

3. Volume Profile - VWAP

    

   1. Removed Average Price option
   2. Only show VWAP line floating above the candles (not showing on horizontal bars)
   3. Applied a little of DSMA algorithm to VWAP
      

4. v2v VWAP (use for Anchored VWAP)

    

   1. Applied a little of DSMA algorithm to VWAP
   2. Updated template
      

I have curiosity if you made your neural network from zero or you are using any standard library. Regards

No... not from zero, from here >>> https://www.mql5.com/en/code/9002

I simply recorded accordingly to my preferred (or based on my limited coding skills with constraints) coding structure that I consistently been using.

Then fused with some algorithms

1. DSMA
2. HMA
3. Jurik
4. and with some optional custom MAs

Cool. Thanks I got some basic C++ of backpropagation because I don't want complicate things but I will try your link too

BPNN... the link that I shared was using the Back Propagation Neural Network. It includes the updated project files (.cpp, .h, .lib... etc) modified by Mladen to work with 64bit machines

Then that will make things easier. Thanks again

This one reminded me of Spuds Stochastic Thread Theory

News : TDZ... just ended the Oscillators Game of Zones ; )─

★ Traders Dynamic Zones ( TDZ ) ...Endgame version 1.54

Attached Image (click to enlarge)

Updates/Modifications:

1. Supercharge JMA with HMA non-lag variation
2. Fast JMA Price default parameter value
3. Updated parameter/label names
4. Updated label names for Price volatility bands
5. Price Line as default divergence
6. Code optimization
7. New template ─ to reflect the updates/modifications

...get it here >>> Post #1