Autoscalp creates and tests proprietary algos.
Autoscalp does not manage third party money.

Proprietary algos 1200.  VM running 11/32. Prospective partner screening time [FIFO]: 5 days.

Updated on 2019/06/21

Autoscalp does not need, neither requests, access to any third party platform or account.
Our algo feed is anonymously routed, one way only, to our network of peers.

Autoscalp owns many proprietary algos, strategies, a market micro-structure based pricing model,
a proprietary data filtering process, data stream filtering process analysis and a dedicated environment.

Autoscalp deposited a proprietary price feed and a proprietary pricing model.

A Patent Pending
has already been filled but then rejected as "object can not be patented" on two core modules.

Algo generating processes have also been deposited as parts or Copyrights through an Intellectual Property Firm.

A Trade Secret has been requested for some core modules through an I.P. Law firm.

Our tools are designed for: liquidity engines, price engines, treasury management engines,
Principal or Proprietary trading firms, asset managers, funds, ECNs, dark pools and investment banks,
specifically avoiding retail sector by design.

Our alpha seeking strategies are language independent, platform independent,
broker independent and reactive to environment modifications.

Our Artificial Intelligence module is based on the results of an international cooperation
and is consisting of our proprietary algos and an undisclosed third party A.I. deep learning environment.

Our Think-tank scenarios, what-ifs and event analysis are independent from governments or banks stand points.

Prospective Partners:
Autoscalp Partnership is restricted to Institutionals only , Autoscalp is not looking for customers.
Please see partnership and disclaimer pages.

Reciprocal signing of Non Disclosure Agreement and Non Compete Agreement contracts are mandatory
ahead of exploring eventual Partnership Proposals.

Business Associate Agreement and reciprocal HIPAA communication standards will also be required
for security reasons at the very beginning of any eventual negotiation.

We also protected our intellectual properties through a technology insurance method.
Autoscalp has asked multiple sources to create independent value estimates of our
know-how, expertise, price engine, algo pool and algo generating processes
then insured them for their nominal value against intellectual property theft attempts .
Such evaluations are basis for setting our stock-options pricing
which are to be issued during a later step in the start-up phase.

 

Latest News:

2019 june 13: our think-tank twitter account was deleted and silenced for unknown reason [link]

2019 june 02: Autoscalp Strategies is evaluating a unit based configuration:

Algos  Network Management Think-tank Public Relations Management Consulting
         
Algo R&D Licensing Management Geopolitical Analysis

Media Relations
B2B

Business Model Improvements
SAAS
TAAL
Web Management Event driven
Algo creation
Institutional Relations     Know-how boutique
I.P. Management
         
internal outsourced internal outsourced internal

 


2018 may 13: Autoscalp rejected the proposal for creating two co-partnership  spin offs .

"A third party proposed to create two companies :
The first one : a joint venture company which will share the patented technology I.P. and other assets .
The second one: as  license distributor of the aforementioned technologies and will also act as cluster
and proxy for other intellectual properties and lead further spin-offs or eventual mergings".

Our internal Business Intelligence task force explored the eventual partnership in detail
and soon discovered that such proposal was masking a very different intent:

a multi billion company operating in the Master Data Management Software sector was trying
de facto to act as a proxy
to alienate Autoscalp algos, know-how and think-tank 
toward an unknown third party .


Thus , as soon such behavior was spotted and properly profiled as a disguised hostile takeover attempt,
their proposal was immediately dismissed and proper action taken.

 

Latest nodes added to network:
Ahmedabad (IN), Thiruvananthapuram (IN), Haidian Qu (CN), Hangzhou (CN), Krasnodar (RU),Satwa (UAE)

 

Our algos are under continuous R&D and testing.
Algo disclosure is limited and compartimentalized, even with our partners.

After NDA is signed:
an example of a complete module top down will be shown:
from the R&D idea to the detailed operative working solution, step by step.

After NCA is signed:
a temporary access to viewing some of our Algos live operations can be arranged.


A few examples of our Algos are:

Algos

 

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 :

Oil Backfire

 


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:

  • dark pool price

  • order book price

  • block trade price

  • sentiment price

  • extraction price

  • trading price

  • delivery price

  • apparent price

  • real price

  • reserve price

  • artificial price

  • deceptive price

  • politically casted price

  • retail price

  • contrarian price

  • volatility driven price

  • more... [undisclosed]

...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".