This tutorial video shows how to export continuous futures data from Sierra Chart to T6, using a free ACSIL script that I put up on GitHub page.

You can view the tutorial here:
https://youtu.be/144texonxfU

Thank you Andrew for the contribution.

Thanks.

I've been playing around with a few others but I'm leaning towards A8 as the docs and demos are very well written.
Kinda like the old style graphics in this engine. If there are any discounts on offer then it may persuade me to make a decision quicker

Hi JCL!

I'm testing the Monte Carlo Reality Check in the Evaluation Shell with
Workshop6c and would like to confirm whether the behavior below is expected.

My MRC configuration is essentially:

Command,MRC
Method,Train
Cycles,10

Workshop6c uses WFO training and NumCores = -2.

I repeated the same Step 5 MRC command in separate Zorro processes and observed:

First experiment:
- 10 completed runs
- 8 runs: P-Value 0.0%, Result is significant
- 2 runs: P-Value 10.0%, Result insignificant
- no non-empty Errors.txt

Second experiment:
- 20 completed runs
- 17 runs: P-Value 0.0%, Result is significant
- 3 runs: P-Value 10.0%, Result insignificant
- no non-empty Errors.txt

I did not observe intermediate p-values. All results were either 0.0% or
10.0%.

After inspecting the Evaluation Shell MRC path, my current interpretation is
that the p-value is incremented in steps equivalent to:

100 / NumTotalCycles

Therefore, with Cycles=10, the p-value resolution is 10 percentage points:

- zero randomized exceedances -> P-Value 0.0%
- one randomized exceedance -> P-Value 10.0%

At a 5% significance threshold, one randomized exceedance therefore changes
the classification from significant to insignificant. This seems consistent
with ordinary Monte Carlo sampling variability rather than necessarily an
Evaluation Shell defect.

Could you please confirm the following?

1. Is alternating between 0.0% and 10.0% expected with Cycles=10?

2. Does the Cycles value include the original/reference cycle, or does it
represent only randomized cycles?

3. Is Cycles=10 intended only as a quick diagnostic setting?

4. What number of cycles is recommended for evaluating a 5% significance
threshold? Would 100 cycles be a reasonable minimum?

5. With Method=Train, is it expected that the MRC retraining overwrites the
normal WFO parameter and factor files, such as Workshop6c.par,
Workshop6c_1.par, etc.?

6. Is there a supported way to set or record the random seed used by the MRC?

7. Can NumCores=-2 change the random sequence or reproducibility of the MRC?
Should a reproducibility diagnostic use a single core?

Each invocation starts a new Zorro process. The strategy, data, MRC
configuration, and Step 1-4 prerequisite artifacts remain the same, but
Method=Train regenerates the normal WFO outputs during every MRC run.

I am using a locally adapted headless dispatcher around the Evaluation Shell.

Thanks.

It will be in the next beta version, 3.12.

Hi all,

I wasn't sure if this was the right place to put this in the forums, but I wanted to give a general status update that I am now operating full time as a freelance programmer, especially to work on trading related projects, especially Zorro scripting and broker plugins, as well as other platforms. Below is a link to an introductory video.
https://youtu.be/pdRFRuGLqTU

Undeads (sound design and music by Volkovstudio)

Thank you for the link. The problem is that trading strateges usually do not exploit statistical properties, but market inefficiencies. Autocorrelation is a market inefficiency, but volatility and return distribution are not. So this synthetic data will not work in the general case, only for strategies that happen to exploit just the statistical property that the generator replicates.

A valid suggestion. Although live performance is no better than backtest performance for predicting the future, it has a psychological advantage. We're considering this for Ranger.

This simulator is a multiscale time series laboratory.

It studies how a changing observed stream can be separated into symbolic state layers, interpreted through multiple temporal contexts, stabilized through diagnostic feedback, and reassembled into a coherent forecast field.

The observed stream is treated as a living temporal object rather than a fixed sequence. Each new state is examined through several symbolic lenses. These lenses include raw state behavior, transformed state behavior, differenced state behavior, seasonal memory, exogenous influence, recursive forecast reuse, and cross resolution agreement.

The system contains a family of contextual models. Each context represents one possible explanation of the current temporal structure. Some contexts describe persistence. Some describe drift. Some describe seasonal recurrence. Some describe transformed coordinates. Some describe external state influence. No single context is assumed to be permanently correct. Instead, every context contributes a partial interpretation of the evolving temporal law.

The simulator converts the observed stream into derived symbolic state variables. The displacement state represents local level separation. The velocity state represents directional increment. The acceleration state represents curvature of change.

The jerk state represents higher order instability. The mass state represents resistance to movement. The stiffness state represents restoring pressure. The damping state represents loss of persistence. The momentum state represents directional carry.

The kinetic state represents active movement concentration. The potential state represents stored displacement pressure. The force state represents combined directional pressure. The power state represents active transfer intensity.

The work state represents accumulated directional effort. The pressure state represents compressed movement intensity. The entropy state represents disorder in local motion. The impulse state represents sudden directional push.
The resonance state represents alignment between natural rhythm and observed rhythm. The strain state represents forecast tension against the observed state. The phase state represents agreement or conflict between interacting cycles.

The frequency state represents runtime modulation of temporal rhythm. The simulator tracks how these symbolic states co evolve under changing dependence, changing variance, changing memory depth, changing regime behavior, changing cross scale agreement, and changing forecast reliability.

The contextual forecast layer does not merely produce a prediction. It estimates structural trust. Each context is evaluated through residual behavior, residual memory, whiteness quality, normality quality, heteroskedastic sensitivity, directional agreement, forecast error persistence, state consistency, health, reliability, and relative dominance.

The simulator is therefore not only a forecast engine. It is a dynamic structure estimator.

Each context acts as a symbolic observer. Each observer sees one part of the temporal field. The system compares those observers, weighs them, weakens unstable observers, strengthens coherent observers, and combines them into a stabilized forecast field.

The model reuse layer studies whether an existing temporal explanation can remain valid as the observed stream changes. The refit layer studies when old structure becomes stale. The transformation layer studies how logarithmic state, differenced state, normalized movement, and seasonal memory alter forecast behavior. The uncertainty layer studies how forecast confidence propagates through the ensemble.

The multiscale layer compares primary and higher resolution interpretations. It measures whether short memory and long memory agree, whether fast motion confirms slow motion, whether pressure accumulates across scales, and whether a regime transition is emerging.

The physics field layer acts as a symbolic interpreter of the time series. It does not claim that the observed stream is a physical object. Instead, it uses physical language as a diagnostic grammar. Movement, force, damping, energy, pressure, entropy, resonance, and impulse are used as structured metaphors for time series behavior.

The regime layer studies how the temporal field moves through calm states, transition states, turbulent states, shock states, and recovery states. These regimes influence noise, damping, stiffness, shock sensitivity, stability, and forecast trust.

The turbulence layer introduces state full disturbance. It avoids purely isolated randomness by allowing disturbance to retain memory. This creates smoother temporal pressure and more realistic local disorder.

The boundary layer prevents uncontrolled state explosion through reflective behavior. When movement exceeds the allowed field boundary, part of the movement is reversed and part is dissipated. This creates a symbolic rebound process rather than simple clipping.

The energy balance layer checks whether movement, storage, dissipation, external force, and accumulated effort remain coherent. This helps separate stable simulation behavior from visually interesting but structurally inconsistent behavior.

The coupled field layer allows displacement, pressure, thermal state, magnetic like alignment, and resonance state to influence each other. This turns the simulator from a single oscillator into an interacting temporal field.

The phase space layer studies relationships between derived states rather than only their movement through time. It examines displacement against velocity, force against displacement, energy against entropy, resonance against pressure, and shock pressure against regime stress.

The time series laboratory layer adds lag memory, recurrence, structural break tracking, volatility clustering, stationarity pressure, seasonality strength, residual aging, cadence quality, temporal gaps, outlier pressure, and forecast blend stability.

The temporal validation layer compares forecast agreement across horizons, residual ladders, cross validation pressure, replay consistency, temporal reserve, clock drift, gap risk, ensemble rank, and warning escalation.

The governance layer tracks quality gates, intervention pressure, resilience, uncertainty, topology drift, objective balance, explain ability, compliance pressure, and simulation health.

The synthesis layer combines observer diagnostics, invariant guards, state contracts, entropy budget, causal mesh, mode consensus, curriculum difficulty, stability atlas, and warning pressure into a higher level diagnostic map.

At the highest level, the simulator is an adaptive symbolic operator on time indexed data. It receives raw observations. It builds state memories. It extracts local geometry. It evaluates contextual models.

It measures diagnostic trust. It detects regime behavior. It forecasts multiple horizons. It compares cross scale agreement. It monitors stability and uncertainty. It applies corrective weighting. It logs diagnostic surfaces.

It renders separate symbolic charts. It produces a self auditing forecast field.

The central goal is to explore how a complex evolving time series can be approximated by a self correcting system that combines classical stochastic process contexts, symbolic physical diagnostics, multiresolution agreement, adaptive weighting, stability controls, and controlled simulation feedback.

This system is designed for research, visualization, diagnostics, and structural experimentation. It is not treated as a simple signal generator. It is treated as a symbolic temporal laboratory.