1. create an algorithm for trading based on 1% value swings

Here’s a basic algorithm for trading based on 1% value swings:

1. Define your target security: Select a security that you wish to trade based on 1% value swings.
2. Set your trading parameters: Define the parameters for your trades, including the amount of capital you’re willing to invest, your desired profit margin, and your stop-loss limit.
3. Monitor the security’s value: Continuously monitor the value of your target security. You can use various tools such as trading software, financial news sources, or chart analysis to track its value.
4. Identify 1% value swings: When the security’s value changes by 1% in either direction, take note of it.
5. Buy or sell: If the value of the security has decreased by 1%, and you believe it will rebound, consider buying the security. Conversely, if the value of the security has increased by 1%, and you believe it may drop, consider selling the security.
6. Set stop-loss limit: To manage risk, set a stop-loss limit for each trade. This is the maximum amount of money you’re willing to lose if the trade doesn’t go in your favor.
7. Take profit: Set a profit target for each trade, which is the desired amount of profit you want to earn. When the security’s value reaches this target, close the trade and take your profit.
8. Repeat the process: Continue monitoring the security’s value, identifying 1% swings, and making trades as necessary.

Note: This is a basic algorithm, and there are many factors to consider when trading, such as market conditions, liquidity, volatility, and more. It’s important to do your research and fully understand the risks involved before implementing any trading strategy.

2. write this is pseudocode

Sure, here’s the pseudocode for the trading algorithm based on 1% value swings:

Note that this is a basic pseudocode and it does not include error handling or other advanced features. Additionally, some variables and functions may need to be defined and implemented separately.

3. write this in python

Sure, here’s an example implementation of the trading algorithm based on 1% value swings in Python:

target_security = "AAPL"  # define the target security

# define the trading parameters
capital = 1000
profit_margin = 1.01  # 1% profit margin
stop_loss = 0.99  # 1% stop-loss limit

while True:
    current_value = get_current_value(target_security)  # get the current value of the target security
    if current_value >= profit_margin:
        sell(target_security)  # sell the security if it has increased by 1%
    elif current_value <= stop_loss:
        buy(target_security)  # buy the security if it has decreased by 1%
    else:
        continue  # otherwise, continue monitoring the value

    # set stop-loss limit and profit target
    stop_loss_value = current_value * stop_loss
    profit_target_value = current_value * profit_margin

    while True:
        updated_value = get_current_value(target_security)  # get the updated value of the target security
        if updated_value <= stop_loss_value:
            sell(target_security)  # sell the security if it hits the stop-loss limit
            break
        elif updated_value >= profit_target_value:
            sell(target_security)  # sell the security if it hits the profit target
            break
        else:
            continue  # otherwise, continue monitoring the value

Note that this is just an example implementation and may need to be adapted to your specific trading platform or strategy. Also, the get_current_value, buy, and sell functions are not defined here and need to be implemented separately based on the API or service you’re using.

And now for some fun

write this for a quantum computer

While quantum computers have the potential to solve certain financial problems more efficiently than classical computers, it’s currently unclear how exactly they could be used for trading based on 1% value swings. As quantum computing is still in its early stages of development, there are few, if any, quantum trading algorithms that have been developed and tested at scale.

However, one possible approach to leveraging quantum computing for trading is by using quantum machine learning algorithms to analyze financial data and identify patterns that can be used to make trading decisions. For example, one could use quantum annealing to optimize portfolio selection and risk management, or quantum algorithms to perform efficient optimization of trading strategies.

Here’s an example of how one could use the Qiskit framework in Python to implement a simple quantum machine learning algorithm for financial prediction:

import numpy as np
from qiskit import Aer
from qiskit.circuit.library import ZZFeatureMap
from qiskit.aqua.algorithms import VQC
from qiskit.aqua.components.optimizers import COBYLA

# Define the target security and trading parameters
target_security = "AAPL"
capital = 1000
profit_margin = 1.01
stop_loss = 0.99

# Define the quantum feature map and backend
feature_map = ZZFeatureMap(feature_dimension=2)
backend = Aer.get_backend('qasm_simulator')

# Define the VQC classifier and optimizer
classifier = VQC(feature_map, None, None)
optimizer = COBYLA()

# Train the classifier on historical financial data
data = np.array([[0.5, 0.1], [0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
labels = np.array([0, 1, 1, 0, 0])
classifier.train(data, labels)

# Use the classifier to make trading decisions
while True:
    current_data = np.array([[get_current_price(target_security), get_current_volume(target_security)]])
    predicted_label = classifier.predict(current_data)[0]
    if predicted_label == 0:
        buy(target_security)
    elif predicted_label == 1:
        sell(target_security)
    else:
        continue

    # Set the stop-loss limit and profit target
    stop_loss_value = get_current_price(target_security) * stop_loss
    profit_target_value = get_current_price(target_security) * profit_margin

    while True:
        updated_price = get_current_price(target_security)
        if updated_price <= stop_loss_value:
            sell(target_security)
            break
        elif updated_price >= profit_target_value:
            sell(target_security)
            break
        else:
            continue

Again, note that this is just a simple example of how one could use quantum machine learning for trading, and many details would need to be filled in depending on the specific application and data available.

So, there we have it. I can now learn how to write code for a quantum computer that might have some bearing on financial insights, with zero knowledge.

Three climate actions you can take today.

One of the challenges we all face in addressing climate emergency is “what can I do that would actually make a difference?”

As individuals, we often feel powerless to make a difference (we need to heat our homes, eat, travel, etc. and it’s tough to really reduce our consumption). Surely it’s up to the finance community to direct money to net zero? Yes, but we are not powerless. On my journey over the years, I’ve spoken to many hundreds of people from the most junior to the most senior to politicians.

My one takeaway is that everyone needs ‘permission’ from someone else (their boss, their board, their shareholders, the members of their, their voters). On one level this ‘outsources’ the responsibility to someone else, but it also points to where the influence lies and leads me to think that the biggest financial impact we can all have as individuals is to do the following three things.

Ask your bank, insurer & pension provider

1. What are their net-zero commitments? (and what do they mean)
2. How will they demonstrate them? (progress and success)
3. How is CEO pay tied to net-zero success? (and what happens if they fail)

Updates

This seems to be emerging

https://www.linkedin.com/news/story/norway-fund-gets-tough-on-net-zero-6107074/

https://twitter.com/nesta_uk/status/1600466016915505155

• Open Finance recommendations https://dgen.net/0/2019/12/19/open-finance-designing-for-cohesion-and-interoperability/
• Detail and webinar on Smart Data https://dgen.net/0/2020/08/15/designing-the-uks-shared-data-infrastructure/

Our transition to net-zero is underway — how can we ensure it is provable, robust, secure and fair?

via The FT we have

The world’s largest oil traders are rushing to plough billions of dollars into renewable energy projects in the next five years, as they speed up preparations for a dramatic shift in the world’s energy mix. Marco Dunand, chief executive of Swiss commodity trader Mercuria, said the company was investing $1.5bn into projects in North America with private equity partners, while Torbjorn Tornqvist, chief executive of Gunvor, said he planned to commit 10 per cent of the company’s equity — hundreds of millions of dollars — over the next two years. “If you want to exist in 10 years’ time and don’t want to be in renewables then I think it is going to be tough,” Mr Dunand told the FT Commodities Global Summit. “We don’t have a choice” but meet the Paris climate goals, he said, calling it the world’s “biggest challenge”

via Forbes we have

renewables investments in Germany and France yielded returns of 178.2% over a five year period, compared with -20.7% for fossil fuel investments

Now join up with the global (excluding USA, but now including China) consensus on the Paris Accord, and the new financial reporting mechanisms (e.g TCFD) for Central Banks in Europe, and beyond.

And, with Icebreaker One, we are developing a national programme — Open Energy — which along with enabling data sharing, has an explicit, embedded target to help deliver the UK’s legally binding mission to be net-zero.

Open Energy is based on the principles of Open Banking, which is still only just getting started… this piece highlights that

the global open banking market generated $7.29 billion in 2018, and is expected to reach $43.15 billion by 2026, growing at a CAGR of 24.4% from 2019 to 2026.

We hope to unlock similar value and growth by opening up access to energy data across not only the sector itself but by connecting energy data across the rest of the systems that must be decarbonised, including transport, the built environment, water and agriculture.

The time for theory is over. The question is how we will instrument the transition to ensure net-zero is provable, robust, secure and fair.

I’ve been receiving a lot of calls from people asking how they can use data to ‘help’. 

To help frame a potential response that isn’t just ‘we can build an app for that’ (that’s very rarely the answer), I’ve been trying to encourage people to first look at user needs and prior work. In times of ’emergency’ there’s a rush for people to want to ‘do something’— let’s harness that energy on useful things rather than squander it on ‘solutionising’?

Here’s a provocation process to ask how we can try and focus on what matters; that can unlock rapid-response from a frontline delivery team who can focus on solutions. At the same time, let’s also avoid just breaking things in the process, and give the frontline team (and the rest of us) the confidence that there is a process following them that will tidy up.

Questions to consider:

• What problems are we really trying to solve — based on actual user needs?
• How will data help these problems? (what data, what analysis, etc?)
• Who should act to convene and lead: a sector, public body, both?
• When are solutions needed and what might be our MVP intervention? (this may not need any data)
• What single-issue could act as an exemplar?

[click to comment or edit]  (or quick-link to this slide http://bit.ly/data-triage)

In City AM

[Re: Why Open Banking is the next finance revolution]

As co-chair of the original Open Banking Standard, it’s great to see the progress being made — and we fully expect this to extend into Open Finance (spanning insurance, pensions and investments). However, access to data in consumer finance is just the tip of the (fast-melting) iceberg. To respond to the impact of climate change we need to apply lessons learned to business data — to make data-at-scale work harder to deliver innovative financing for a carbon-zero future. With COP26 being held in the UK this year, we have an opportunity to combine our industrial strategy, climate targets and data infrastructure to shape our investment strategies, reveal efficiencies and better understand risk. Sorting out our ‘data plumbing’ will help power world-class analytical capabilities (e.g. artificial intelligence) to help our financial and environmental worlds align.  

Gavin Starks,

Founder, Icebreaker One

The Financial Conduct Authority set up an Advisory Group on open finance to discuss the potential of extending open banking-like data-sharing to a wider range of financial products.

It has now launched an open consultation: https://www.fca.org.uk/firms/advisory-group-open-finance

Our contribution was in Chairing the Cohesion and Interoperability working group, the summary output of which is embedded below.

Document: Open Finance — Advisory Summary on Cohesion and Interoperability for the FCA

PDF version: download here

Open Banking—is a regulated standard that addresses data sharing across the banking sector. 

It addresses: 

• Rights
• Liability models
• Dispute resolution and redress
• Consent
• Security
• Legal frameworks
• Usability 
• Logistics
• Technology architecture
• Operating principles
• Myriad related issues

Open Banking was created by convening teams to develop common principles and practice for sector-wide data sharing. They included existing and challenger banks, trade bodies, fintechs, Treasury and regulators. In the UK, this led to the creation of an independent non-profit (funded by the banks) to develop and take the standard to market, with a directory of accredited organisations using it. 

The standard was (and is) developed openly—as a result, it has helped to catalyse initiatives around the world.  Similar initiatives now exist across Australia, Bahrain, Europe, Hong Kong, India, Japan, Mexico, Malaysia, New Zealand, Rwanda, Singapore and the USA.

This work is being extended to ‘Open Finance’. This would further include Investments, Pensions, Savings, Consumer Credit, Mortgages and General Insurance. 

We are also working on its extension into other sectors such as energy and environment (through IcebreakerOne.org). 

We are working with the UK regulator, the Financial Conduct Authority, to help shape the direction of this work.

After an 18-month industry consultation, we opened Icebreaker One at the UN Climate Action Summit in New York.

–> More information here on the new IcebreakerOne.org website

Between this and Open Banking/Finance, we’re going to be quite busy at Dgen HQ.

We announced of our first open consultation for IcebreakerOne at the UN DRR Global Platform on 13 May 2019.