Autoscalp produces proprietary algos.
Autoscalp does not need neither requests access to any third party platforms or accounts.
Our algo feed is anonymously routed, one way only, to our network of peers.

Autoscalp owns many proprietary algos and strategies and a market micro-structure based pricing and analysis process.

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 .

Our tools are designed for: liquidity engines, pricing 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:
On 13/05/2018 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 cities added : Al Ahmadi (KW) , Ḩawallī (KW) , Ataşehir (TR) , Ümraniye (TR)

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:

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:

• autonomous raw intel gathering agents
• non relational data processing and event driven profiling
• situational awareness models
• biased dataflows spotters
• counter-profiling models
  
• analytics and dynamic algo rules
  
• market pricing models
• profiling of syntethic price dynamics
• filters against biased publicly accessible price streams
• counter-manipulation techniques against non transparent price feeds
• counter-baiting techniques against manipulated price feeds
• counter tactics against market players typical behavioral models
• simulator of effect distributions of disruptive market behaviors
  
• sniffers to take advantage of inefficient market micro-structures
• ssniffers to take advantage of exploitable externalities or events
• sniffers to take advantage of exploitable market inconsistencies
• sniffers to take advantage of network vulnerabilities and subsequent lags

All the above are PROPRIETARY modules,
meanwhile eventual low latency and ultra low latency modules
are from third parties thus are not disclosed as by NDA agreements.

An example of asymmetic information we exploit:

the OHLC model flaw:

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 opportunism
[from a monopolistic position who injected that into markets]

Banks willfully and knowingly refused to replace it
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 it is not.

OHLC is faking the dataset as follows:
in OHLC every candle has: Open, High, Low,Close prices.

As you choose a timeframe you ARBITRARILY INJECT
the Open and Close datapoints depending on
your subjective perception of a theoretical timeframe

so, while High and Low are real datapoints , Open and Close are artificious.

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.

In our view: being the worldwide accepted OHLC model flawed
( sell-side biased ) , we prefer using our proprietary one.

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

our pricing model is far more efficient than OHLC, by design.

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:

...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".