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An algo based on our proprietary pricing model
typically consists of many PROPRIETARY modules
Which run in a dedicated environment and relative virtualized architecture.
Typical PROPRIETARY modules of our algos are:
- Creation of alternative market pricing models, decentralized, delocalized, granular
- autonomous raw data gathering agents, on price sources, market players...
- autonomous raw intel filtering [humint-elint, AI simulated]
- unmasking indirect market movers and hidden critical events
- reactive situational awareness models (market and geopolitical driven)
- actionable exploitation of biased datastreams spotters and corrective filters
- actionable exploitation of market players profiling and counter-profiling models
- actionable exploitation of market analytics and dynamic based self-optimizing algo rules
- actionable exploitation of unfair disruptive market behaviors
- actionable exploitation of direct and indirect manipulative behaviors
- dynamic blocking and filtering of lagged or otherwise manipulated price streams
- dynamic blocking and filtering of flawed price feeds and their sources
- spotting and exploiting biased syntethic price dynamics
- spotting and exploiting non transparent price feeds
- spotting and exploiting manipulated price feeds
- spotting and exploiting market players typical behavioral models
- spotting and exploiting market inefficiencies (feed pinging) both from seller and buyer side
- spotting and exploiting lack of expertise (flaw scanning) both from seller and buyer side
- dedicated sniffers to take advantage of inefficient market micro-structures
- dedicated sniffers to take advantage of exploitable externalities or events
- dedicated sniffers to take advantage of exploitable market inconsistencies
- dedicated sniffers to take advantage of network vulnerabilities and subsequent lags
Eventual integration with third party modules such as
low latency and ultra low latency modules, AI enhancements etc.
won't be disclosed as by NDA agreements.
An example of asymmetic information We exploit against providers:
the OHLC model flaw:
 The OHLC model has been forcefully injected into market analysis in 1920s
by banks to serve their compound interest model , banks main source of revenue,
with the consequence of blocking and replacing any alternative pricing model ever since.
Bank supported university research went solely in that direction for a whole century
and that made the OHLC model the only standard pricing model
but
as any other pricing model it was maybe correct for a certain period,
it has become obsolete around 1970s . So why is till in use today ?
Views on OHLC models can be summarized as:
- Economists view: OHLC is just a comfortable convention.
- Market view: OHLC is a worldwide standard.
- Our view: OHLC means unilateral deception and opportunism
OHLC was injected as a trojan in any platform through the mere abuse
of monopolistic position of the providers who are not verified
on the tools they provide to their customers from a trading efficiency
standpoint but just from a legal compliance one.
Since way too often market compliance rules have been concocted
by banks themselves against investors and customers,
such intentionally flawed tools tend to pass the tests same banks
put as benchmark and standards.
Banks and brokers willfully and knowingly refused to replace OHLC
even if is easily demonstrated as clearly flawed because brought
them a steady stream of profits,
very often OHLC model acts against clients and speculators as
they think a certain price is real while is definitely not so.
Prices you see as OHLC are both artificious and artificial.
OHLC candles make the price datastream fail as follows:
every OHLC candle has 4 price datapoints: Open, High, Low,Close.
As you choose a timeframe you ARBITRARILY INJECT YOURSELF
the Open and Close datapoints depending on
your subjective perception of a theoretical timeframe you choose.
So, while High and Low are real datapoints , Open and Close are artificious.
Since it was you who injected the two FICTIOUS datapoints you can't blame
the data provider which uses that as waiver: you chose it, your problem.
Conclusion: using OHLC
you add two artificial and subjective data every 4 datapoints ,
for each candle building the price history you are evaluating:
fact which makes any further analysis on that time-serie
not only useless but misleading also.
Being the worldwide accepted OHLC model flawed ,
very heavily sell-side biased, We prefer using our proprietary tools
who can easily predate it.
Our pricing model is far more efficient than OHLC, by design.
Examples of exploitability of the OHLC model flaw:
-in markets NOT ruled by compound interest based clearings
-in transactions where rollover is forbidden
-in swap free operations
-in places where compound interest is illegal
Generally speaking OHLC should be avoided in any situation
where investigating the originating price source is forbidden.
Finding the quote source originating a price is unpractical for the average operator:
banks and exchanges don't allow you check or question their price feed sources
once they sign it, being the price internally made up, through the questionable
practice of order crossing, feed skewing,spiking or similar, or even worse
being the feed merely routed from a third party, an external providers
who might simply have brewed it on request.
Since you often sign a contract that expressly forbids you to do such search
(price arbitration clause) and get their price as they give it to you (best quote).
So, if you don't know who is really selling you a price would be a reasonable assumption
to question it till its roots (teorethically but not quite often, an order match)
but is clearly unpractiable to do a realtime
backtrack of every single quote to the true price provider for each quote casted.
OHLC flaw is interestingly the very same flaw many digitally signed price chains inherited:
not by chance, as the originators know this flaw is very profitable on their side:
if you can fully check a price to be compliant to the rules, does not mean the rules
are neither transparent nor fair, hiding that is often
the true price chain provider hidden purpose
...so you end up "trusting the OHLC candle" as a "standard convention"
which is precisely where OHLC flaw impacts:
as you stop searching the price source,
your counterpart just bets on that.
An example of a our proprietary pricing model
running our speculative algo on Crude Oil :
Algo main task:
defend oil buyers or sellers from speculative oil price fluctuations,
and relative volatility issues.
Algo was specifically designed in 2016 for :
Oil tanker management companies. oil storage facilities and oil trading companies.
Such algo succesfully identified the incoming oil price collapse which
essentially produced a very effective early warning.
Synth Oil pricing model components which took part in the algo pool
were definitely not just bid and ask:
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dark pool price
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order book price
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block trade price
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sentiment price
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extraction price
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trading price
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delivery price
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apparent price
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real price
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reserve price
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artificial price
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deceptive price
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politically casted price
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retail price
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contrarian price
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volatility driven price
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more... [undisclosed]
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...dynamically adjusted given externalities and market environment dynamics.
Algo class:
active counter-profiling.
Model:
algo actively searches, finds, identifies and exploits price loopholes, price lags, recognizes media disinformation, profiles price manipulation , price stuffing and other deceptive practices typical of centralized oil pricing players and models.
Strategy:
counter-profiles the typical behavior of a price arbitraging speculator acting both in a delocalized pool and in a centralized oil market.
Special config:
Algo tracks real events, thus it does not stop posting oil quotes on week-ends: just because banks are offline doesn't mean oil price stays still or price dynamics stop. Another major mistake our competitors do is to confuse the real oil price with the one you see on trading platforms.
Reason:
oil market is creating an artificial spread between the real price the apparent price. While our Backfire model knows there ar many concurrent oil prices: our competitors, luckily, keep ignoring such fact and keep using a single centralized oil price as source. We call that price a pseudo-source. Just because their price is centralized doesn't mean is real.
Components can be automatically weighted, enabled or disabled depending on market situation (see above crude oil price structure sub-components).
Such level of detail greatly contributed to obtain the very effficient Algo test results we had.
Algo test results are avaliable to download for our think-tank partners. Algo test results are updated weekly.
External algo evaluators comments are undisclosed as by NDA but if were to be summarized would likely be this kind of statement:
"Algo performed slightly better than expectations with a very effective risk control process".
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