You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Canonical vs Non-Canonical Indicators

The question has shown up several times more or less like this:

How is this or that best/canonically implemented with backtrader?

Being one of the goals of backtrader to be flexible to support as many situations and use cases as possible, the answer is easy: "In at least a couple of ways". Summarized for indicators, for which the question most often happens:

100% declarative in the __init__ method

100% step-by-step in the next method

Mixing both of the above for complex scenarios in which the declarative part cannot cover all needed calculations

A quick view of the built-in indicators in backtrader reveals that all of them are implemented in a declarative manner. The reasons

Easier to do Easier to read More elegant Vectorized and even-based implementations are automatically managed

What ?!?! Auto-implemented Vectorization??

Yes. If an indicator is implemented entirely inside the __init_ method, the magic of metaclasses and operator overloading in Python will deliver the following

A vectorized implementation (default setting when running a backtest)

An event-based implementation (for example for live trading)

On the other hand and if any part of an indicator which is implemented in the next method:

This is the code directly used for an event-based run.

Vectorization will be simulated by calling the next method in the background for each data point Note This means that even if a particular indicator does not have a vectorized implementation, all others which have it, will still run vectorized

Money Flow Index: an Example

Community User @Rodrigo Brito posted a version of the "Money Flow Index" indicator which used the next method for the implementation.

The code

Non Canonical MFI Implementation class MFI ( bt . Indicator ): lines = ( 'mfi' , 'money_flow_raw' , 'typical' , 'money_flow_pos' , 'money_flow_neg' ) plotlines = dict ( money_flow_raw = dict ( _plotskip = True ), money_flow_pos = dict ( _plotskip = True ), money_flow_neg = dict ( _plotskip = True ), typical = dict ( _plotskip = True ), ) params = ( ( 'period' , 14 ), ) def next ( self ): typical_price = ( self . data . close [ 0 ] + self . data . low [ 0 ] + self . data . high [ 0 ]) / 3 money_flow_raw = typical_price * self . data . volume [ 0 ] self . lines . typical [ 0 ] = typical_price self . lines . money_flow_raw [ 0 ] = money_flow_raw self . lines . money_flow_pos [ 0 ] = money_flow_raw if self . lines . typical [ 0 ] >= self . lines . typical [ - 1 ] else 0 self . lines . money_flow_neg [ 0 ] = money_flow_raw if self . lines . typical [ 0 ] <= self . lines . typical [ - 1 ] else 0 pos_period = math . fsum ( self . lines . money_flow_pos . get ( size = self . p . period )) neg_period = math . fsum ( self . lines . money_flow_neg . get ( size = self . p . period )) if neg_period == 0 : self . lines . mfi [ 0 ] = 100 return self . lines . mfi [ 0 ] = 100 - 100 / ( 1 + pos_period / neg_period )

Note Kept as originally posted including the long lines for which one has to scroll horizontally

@Rodrigo Brito already notices that the usage of temporary lines is (all lines except mfi ) is something which probably admits optimization. Indeed, but in the humble opinion of the author*, actually everything does admit a bit of optimization.

To have common working grounds, one can use the "Money Flow Index" definition by StockCharts and see that the implementation above is good. Here is the link:

With that in the hand, a quick Canonical implementation of the MFI indicator

Canonical MFI Implementation class MFI_Canonical ( bt . Indicator ): lines = ( 'mfi' ,) params = dict ( period = 14 ) def __init__ ( self ): tprice = ( self . data . close + self . data . low + self . data . high ) / 3.0 mfraw = tprice * self . data . volume flowpos = bt . ind . SumN ( mfraw * ( tprice > tprice ( - 1 )), period = self . p . period ) flowneg = bt . ind . SumN ( mfraw * ( tprice < tprice ( - 1 )), period = self . p . period ) mfiratio = bt . ind . DivByZero ( flowpos , flowneg , zero = 100.0 ) self . l . mfi = 100.0 - 100.0 / ( 1.0 + mfiratio )

One should be able to immediately notice

A single line mfi is defined. No temporaries are there.

Things seem cleaner with no need for [0] array indexing

No single if here or there

More compact whilst more readable

Should one plot a graph of both run against the same data set, it would look like this

The chart shows that both the Canonical and Non-Canonical versions show the same values and development, except at the beginning.

The Non-Canonical version is delivering values from the very start

It is delivering non-meaningful values (100.0 until it deliver 1 extra value which is also not good) because it cannot properly deliver

In contrast:

The Canonical version automatically starts delivering values after the minimum warm-up period has been reached.

No human intervention has been needed (it must for sure be "Articifial Intelligence" or "Machine Learning", ... pun intended)

See a close-up picture of the affected area

Note One could of course try to alleviate this situation in the Non-Canonical version by doing this: Subclassing from bt.ind.PeriodN which already has a period param and knows what to do with it (and calling super during __init__ )

Notice also that the Canonical version does also account, like the step-by-step next code for a possible division-by-zero situation in the formula.

Non Canonical Division By Zero Handling if neg_period == 0 : self . lines . mfi [ 0 ] = 100 return self . lines . mfi [ 0 ] = 100 - 100 / ( 1 + pos_period / neg_period )

whereas this is the other way to do it

Canonical Division By Zero Handling mfiratio = bt . ind . DivByZero ( flowpos , flowneg , zero = 100.0 ) self . l . mfi = 100.0 - 100.0 / ( 1.0 + mfiratio )

Instead of having many lines, a return statement and different assignments to the output line, there is s single declaration of the mfiratio calculation and a single assignment (following the StockCharts formula) to the output line mfi .

Conclusion