ALGORITHM DEVELOPMENT PROCESS

Developing stable, accurate and performant algorithms brings some extra challenges, extending the classical “Software Engineering”. For this special purpose, we have built the process of developing and tailoring the algorithms called “Algorithm Development Process” (ADP), which ensures flexibility, high pace and reliable deliverables. To our knowledge, this process is unique and results in the quantitative models to be more stable, well tested and documented.

A SECTION OF OUR ALGO INVENTORY

Cash Account Aggregation

Get an aggregated view on all cash accounts.

Savings Rules

Define rules for savings based on account transactions and volumes.

Target Saving

Define target savings as objects of desire.

Forecast-based Savings

Suggestion of savings rate based on forecast of Net Income.

Asset Categorization

Categorization based on product type, region, sector, liquidity etc.

Portfolio Aggregation

Get one integrated view on all your securities deposits.

Asset Allocation (BL)

Allocation based on the (extended) Models of Black/Littermann/Markovitz.

Forecast-based Savings

Allocation based on the (extended) Model of French/Fama.

Risk bearing capacity

Calculation of the level of acceptable risk for the customer.

Risk Profiling

Set of 40 dialogues identifying desired volatility, drawdown and durations.

Stresstesting

Simulation of various market events and their effects on the portfolio.

Efficiency curves

Comparison of risk and performance for existing and planned investments.

Rebalancing

Continuous monitoring and adoption of the portfolio.

Rebalancing (Duration)

Rebalancing based of Risk Profile based on investment duration.

FinStata Lib

Library with over 50 indicators (e.g. drawdown, v@r, greeks).

Product Selection

Filtering of ETFs & Funds based on 50 criteria (see FinStata Lib).

Cash Account KPIs

Performance Indicators calculated based upon a client’s cash accounts.

Core-Satellite Allocation

Integrate a diversified portfolio with targeted special investments.

Peer Group Cash Account

Cash-account based benchmarking (e.g. based on income, outcome).

Benchmarks

Comparison against relevant indices.

Event Monitor

Scans for extraordinary income or outcome and triggers a dialogue.

Trend Indices

Scans for products matching predefined trends (like Robotics, Bio, etc.).

Portfolio Transition

Suggestion of changes for transforming a current portfolio into a desired one.

Cost Approximation

Approximation of product costs (TER) within the current portfolio.

DATA DRIVEN ANALYSIS

To improve our software and provide additional benefits to our clients, we analyze financial and non-financial market data as well as cash accounts & securities deposits data and develop processes in order to comply with future regulation. In concrete terms our Labs do the following:

  1. Market Data: We derive market indicators and trends from financial markets. The trends are also bundled in our tool called “Trendvest”.
  2. Account Data: We analyze cash accounts & deposits to find patterns in customer actions by applying learning algorithms. Insights are transported as rules into our software.
  3. Future Regulation: Together with our Venture Lexcube we test upcoming regulations early on within our software workflows.

DATA DRIVEN RESEARCH FOR TRENDS 

To provide our clients an emotion-based alternative to stocks and bonds, we derive trends such as e.g. ageing society, business software, robotics or electromobility from research & financial market data.

Trend Search

Index Development

A. Fincite Push

150 automated searches
for patterns in market data.

Trend definition
and the characteristics.

Three-dimensional clusters
identification of trends.

Building a long list
of potencial products.

B. Request Push

Manual trend hypothesis
on trends by the client.

20+ criteria for selecting
the right products.

Thorough validation of the
trend hypothesis.

10+ weighting strategies
for different risk profiles.

C. Research Push

Fincite Research Analyst
with access to Trend Libs.

Setup of the weighted
indices.

Thorough validation of the
trend hypothesis.

Historic and scenariobased
backtesting.

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