Broker to Trading

Trading narratives

One reason that traders may be wary of trading narratives is because they are unstable. A stable narrative would be proven true or false over a selected time frame. However, narratives chosen for trading embody speculation. This means that they cannot have a certainty or even a likelihood of being true. The more speculative they are, the less likely it is that the market has already taken it into account. So a narrative can easily collapse as new information invalidates the reasoning behind it, and the more attractive they seem in terms of outcome, the more this might be the case.

This still leaves time as a factor. Either shortening it to a point, with news trading, or elongating it with a speculative position trade. Thus the narrative may seem unlikely or highly speculative now, but over time it might develop. As time progresses, the narrative can be changed, as necessary. This leaves plenty of room for creating a narrative that might better withstand the test of time, what might be termed stabilising it. But how may this be done?

A technical indicator provides a signal, based on past trading patterns, and it can be used to enter, exit, and manage a trade. But technical indicators are shallow, as are trading robots, in some sense, which typically use indicator signals. Trading robots make up for the simplicity of the signal by making typically many trades and leveraging any effectiveness in the trading signal, itself based on a trading pattern (i.e. a signalled event that has had a probability over time of working out - that's what the robot is trading on).

A trading narrative is normally a causal chain, stating that A might happen because B + C + ... The causality is crucial because it provides a clear rationale for the action to be taken. However, unlike technical indicator signals, narratives are more like clues put together from existing data. But they can be seen as more complex and better adapted to some markets than indicators, with caveats.

Technical indicators seem dynamic as they can provide a signal based on market conditions at the time of the trade. However, the basis upon which the signal is generated is a calculation of the market reflecting past trading patterns. For volatile, complex markets like Forex, where narratives can seem highly unstable if they can even be put together, indicators provide a way to trade (or not to trade) whatever the market conditions at the time. However, the dynamic nature of the market can easily invalidate technical signals, based as it is on past patterns.

It might be said that volatility is the enemy of a narrative, as it can obfuscate or obliterate a market reaction aligned with the narrative. Narratives are arguably better suited to markets where there is a time frame that is significantly longer than day trading. For example, a belief in a technology such as quantum computing becoming a better, more useful technology and thus having applicability in various industries may take years to play out. This kind of narrative is contingent on things that may not have happened yet. The future contingency provides unstable, speculative points that can be proved true or false, allowing the narrative to be adjusted if necessary.

Thus, narratives are arguably more suited to position trading, where the trader may build a position based on a narrative. Indeed, for position trading, narratives may be a more optimal way to find a trading basis, as the narrative itself can capture the steps necessary for its completion, i.e. the narrative can mirror the structure of a trade (or at least a guess about this).

The act of building the position may make use of indicators and other signals to trade in and out of positions. In future contingent narratives, this may be important, as there may be little to support the sector in the current time frame, leaving it prone to the flow of money in and out of the market. Thus, in effect, the narrative can seem unstable, even if it works out in the long run.

Put simply, an indicator at least provides a basis for trading volatile markets, especially on day trading time frames, but a trading narrative provides a tailor-made way to trade on the longer term, assuming the market can let the narrative play out. This means that markets like Forex can be problematic for narratives, as their inherent volatility, complexity, and lack of directionality, from short to long-term, may make the application of causal patterns problematic. However, they are used in ways that seek to pin down the market in the short term.

Is news trading a narrative?

News trading relies heavily on causal chains, but ones which are expected to play out in very short-term time frames. News trading perspectives can vary. Traders may trade in the run-up to or after the news release. However, many trade at the time of release, placing a position shortly before it and exiting shortly afterward.

News trading is an example of how unstable narratives are, because what is being traded is a market reaction to the news. So really, it is more about the state of the market, rather than the truth of the narrative. The run-up to the event can be about the narrative, or at least a narrative built around a consensus about what the data will be as can be the time after.

However traders may be focused on the potential for a rapid, directional move that side-steps the normal complexity of the market. But what they may get is more complexity, in a oscillation, even if the narrative is correct. The problem is that the state of the market matters in a way it may not matter so much in a long-term position trade.

LLMs and narratives

LLM AI chatbots may be useful to develop a narrative. LLMs are responsive to their input as well as grounded in their database, which will typically encompass a great deal of information about trading and market information. In a multi-turn conversation, it might be possible to examine the plausibility of a narrative. While chatbots will not necessarily say a narrative is bad, one can get a sense of whether it seems consistent with its database and reasoning. This is not a confirmation, as the reasoning can be ill-founded and simply wrong, but it can provide a way to check the plausibility of a trading narrative and optimise it.

To some extent, LLMs seem to reflect the opinions of the user, but they reflect it through a prism of vast amounts of information, and a neural net trained for correlations. So what comes out is an altered view that may be useful to try and produce a more grounded, reasonable causal chain (kind of like weighing the evidence, for something subjective and hypothetical). Chatbots that provide the causal chain for their analysis may also be useful as a way of checking the coherence and plausibility of each step in the trader's narrative.

Case Study Example: Quantum Computing vs LLMs

Quantum computing is an example of a narrative that is highly subject to volatility. Partly, this might be because the technology is an undefined factor, and the technology is key, rather than marketing. As this site discusses, quantum computers do not currently exist in the sense that a classical computer exists. In short, it is a developing technology with unknowns about whether it can be viable. This instability creates an edge-tech that has been subject to intense volatility, where it can be traded on.

LLMs are an example of a technology that has created, stabilised, and enlarged narratives and re-energized views of companies, simply because we can all see this tech in action, and how novel and seemingly effective it is, without being sure about its long-term effect. This is difficult to do with quantum computing, except to look at intriguing pictures of isolated qubits. The fact that quantum computing does not outperform classical computing yet, means that there is no benchmark to stabilise this narrative.

But narrative-driven it is, as the effect of quantum computers may be profound in many industries. AI came into view with LLMs (even though it has existed for many years) and that created a powerful narrative. When quantum computing 'comes into view' with a breakthrough, then that may drive new narratives, stabilise some, and enlarge others.

Stabilising narratives

However, one does not want to wait till the breakthrough happens, just as one does not want to wait for the news event to be released. Narratives can be seen as a news trade, but one that is not tied to a specific time or a focused outcome. News trading can itself be seen as an attempt to stabilise a narrative, by tying it down to a time and a specific release. However, this also means that narratives can price in the result (as the time is known).

So narratives that are not tied down to a specific time are protected in a way, but are prone to volatility until the narrative becomes true or false. However, unless this is sudden, then they can also be priced in. In a sense, a narrative is something that must have unstable points in it, or it will not be of much use when trading.

So what is it one is doing when discussing a narrative with a chatbot? It seems that one is looking for its relative plausibility. In effect, is it a coherent narrative, even if it is founded on suppositions? What this means, is tying down what can be tied down, and one way to do this is to turn the idea through a technology that is designed to be responsive to the input but that also reflects it through its database.

This makes a chatbot potentially a tool that may be used with other more established trading tools. But it is a tool with caveats, as it is not built on being accurate per se, but rather on being conversational and logical. So the information it provides should be fact-checked. But as we noted, its capacity for conversation and logic is something that may be useful as a trading tool for developing narratives.

Trading narratives: From idea to application

As we have seen, trading narratives can be applied in different ways, but to trade into the market, the trader needs a provider. CFD providers tend to have different features. For those who want to construct their trading narratives and apply them in self-directed trading, the trader may wish to explore CFD providers that provide platforms for those who make their trades. Trading narratives are nuanced and subjective, thus they are not suited to robots.

However the trader may wish to extract a quantitative strand from a qualitative narrative. Looking to automate a trading narrative is something that becomes more a subject for study in AI, as it involves subtle, human-like reasoning. Bear in mind, that part of a trading narrative, as we have discussed, is the capacity to alter it, sometimes drastically, sometimes in more nuanced ways, as contingencies change. This is not something trading robots are good at, but it may be something that machine learning can work with.

However, if the trader wishes to use robots, they can try and look for narrow quantitative perspectives on their trading narratives and try and translate this into an indicator signal. Thus, the trader may wish to try CFD providers which offer both automated and self-directed trading. So we have compiled several CFD providers into the list below for the trader to try and see if they might satisfy their needs with regards to the application of trading narratives into the market.