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Page 1: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

AlgoDynamix

Page 2: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification
Page 3: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Financial risk forecasting (market risk) using no historical data! Existing clients across asset/portfolio management and trading

Ability to deal with disruptiv

e events

Historical price & correlations

News & data feeds

Software sophistication

Real time crowd analytics

Pro

duct

dif

fere

ntia

tion

Page 4: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

AlgoDynamix analytics engine Software identified

´clusters` of user-behaviour

Within each cluster, users have comparable feature sets

Cluster identification amongst noisy buyers and sellers is part of our unique core capabilities

SellerBuyer Cluster

Nor

mal

day

Time16 Jun 18 Jun 20 Jun 21 Jun

170

160

150

180

14 Jun

USD

Page 5: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

AlgoDynamix analytics engine Software identified

´clusters` of user-behaviour

Within each cluster, users have comparable feature sets

Cluster identification amongst noisy buyers and sellers is part of our unique core capabilities

SellerBuyer Cluster

Up

Flag

Time

170

160

150

180Directional Up or Down Flag:

Price S&P 500 USD

16 Jun 18 Jun 20 Jun 21 Jun14 Jun

Page 6: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Example: the ‘Trump chart’

Financial risk forecasting providing hours or days advance warningof major directional market movements

Coverage: global equity markets

Dir

ecti

onal

ris

k fo

reca

stin

g

Page 7: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Case study own US fund : very good risk adjusted returns

Enter and exit S&P 500 ETF about 15 times per year, long only positions above results over 3 years benchmarked against S&P 500 buy & hold position

0.00

20.00

40.00

60.00

80.00

100.00

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200.00

Donnerstag, 2. Januar

2014

Mittwoch, 28. Mai

2014

Freitag, 17. Oktober

2014

Freitag, 13. März 2015

Mittwoch, 5. August 2015

Montag, 28. Dezember

2015

Freitag, 20. Mai 2016

Mittwoch, 12. Oktober

2016

Mittwoch, 8. März 2017

ALDX PI (ultra low vol.) versus S&P 500 benchmark

Goo

d ri

sk a

djus

ted

retu

rns

Page 8: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Product details

Del

iver

y: E

mai

l, Ex

cel,

Web

, AP

I

Cloud based (including optional API) solution does NOT require any client data white labelling solution available

Page 9: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Ove

rvie

w

next

ste

ps

Live (production) deployment with tier 2 investment banks and asset managers

Reference clients include German based ACATIS Investment GmbH (AI Fund)

The ASK:

Deployment internally within IB/Asset management; ‘quick and easy’ PoC

Global distribution to UBS clients (including hedge Funds), a very unique differentiator!

Page 10: AlgoDynamix fileAlgoDynamix analytics engine Software identified ´clusters` of user-behaviour Within each cluster, users have comparable feature sets Cluster identification

Future of Finance EMEA (London) final

Questions?