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August 2021

Trade platform

IBM finds that ASX outage is due to business platform not ready for go live

The Australian Securities Exchange (ASX) experienced “software glitches” when it went live with the refresh of its trading stock platform in November last year, forcing the exchange to suspend trading. trades.

At the time, the exchange said its technology provider Nasdaq, as well as independent specialist clients and third parties, performed extensive testing for more than a year on the ASX Trade system, including four dress rehearsals, in view of its sending into nature.

The technology used, he said, was the latest generation of a trading system developed by the Nasdaq and used around the world.

Following the blackout, the Reserve Bank of Australia (RBA) and the Australian Securities and Investments Commission (ASIC) requested an independent review, and ASX saw fit to turn this responsibility over to IBM.

Never Forget: IBM Blasted By ABS For Not Handling The DDoS Census

On Monday, IBM made 17 recommendations to ASX and discovered a number of shortcomings in the project, including noting that the business platform was not ready for commissioning.

“Factors that suggested the ASX Trade system was not ready to go into service given ASX’s almost zero appetite for downtime. This was the case even though the formal implementation readiness processes were completed and verified by multiple parties with no objection to go live, ”IBM found.

“There were gaps in the rigor applied to the risk management process and expected project delivery issues for a project of this nature, and the risk and issue management, project compliance with ASX practices, requirements of the project and the test strategy / planning of the project have not been accepted industry practice.

“It was not reasonable to expect that the test plan used would satisfy ASX’s almost zero appetite for downtime.”

According to Big Blue, seven factors suggested the platform was not ready for go live, including historical software product quality metrics, additional testing needs noted, quantity of open defects, gaps in end-to-end test coverage. , the proximity of the freeze windows to year-end changes for participants, the risk probability ratings, and the lack of evidence of challenges to the risk rating or commissioning.

“Last November’s market blackout did not meet ASX’s high standards,” ASX CEO Dominic Stevens said on Monday. “We thought the software was ready for commissioning, as was our technology provider Nasdaq. There were clearly some issues, which was particularly disappointing given the significant progress we’ve made on resilience in recent years. “

IBM also concluded that the project could have benefited from additional and independent review.

He determined that there were gaps in the rigor applied to the risk management process and issues with project delivery, such as opportunities to identify additional missed risks, differences in delivery risk models project and enterprise delivery risk processes, with the project not receiving risk resources with greater technical project experience from which it could have benefited and governance being transferred to a group that had a wide range of responsibilities.

“The change diluted the focus on the project,” IBM said.

The review did find some bright spots, however, with IBM claiming that the ASX met or exceeded industry leading practices in 58 of 75 of the capabilities assessed.

“We acknowledge the findings of the report. It is fortunate that the ASX has met or exceeded industry leading practices in most areas. But the report highlights some important areas for improvement and we will respond to any of its recommendations.” , Stevens added.

“ASX is well advanced in developing a detailed response plan to be executed over the next 12-18 months, and we will be asking the independent expert to review our actions to respond to his recommendations. Our execution of this work program will be under the supervision of ASIC and RBA. “

IBM said developing the business case for the project and managing project change was beyond accepted practices; that the project had and had access to sufficient financial, time, human and technological resources at all stages of implementation to achieve its objectives; that communications with key stakeholders were appropriately managed by the ASX; and that the incident management actions taken by the exchange were appropriate.

In 2018, the exchange was asked to strengthen its risk management practices following an “unprecedented” hardware failure in September 2016 which led to the crash of its stock market. According to ASIC, the actions taken by ASX in the 2020 incident were appropriate and reflected lessons learned from the 2016 incident.

“ASX takes the resilience and reliability of its markets very seriously. That is why we have immediately contacted our regulators to order this external review and we will act on all of its recommendations. It’s also why we’ve already taken steps to change our project delivery practices, “Stevens continued.

“The changes we have made to our management structure are aligned with these goals.

“Driving technological change is difficult and creates a risk of transition. No market will function without incidents or outages from time to time. Nevertheless, all the failures are regrettable. “

Regulators expect ASX to apply information from IBM’s findings across the exchange to ensure that existing and proposed projects, including the CHESS replacement program, are managed and implemented. appropriately.

ASIC is also undertaking a separate investigation into the ASX Trade outage to determine whether ASX has fulfilled its obligations under its Australian Market License.

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Market trading

Create a market trading bot using Open AI Gym Anytrading


The main focus of AI is the development of computational functions associated with human intelligence, such as reasoning, learning and problem solving, which can be particularly useful for markets. Trading and investing in the market only requires a series of reasoning and calculations, based on data and solving the problem of predicting the future direction of current stock prices. Fundamental and manual technical analysis is going out of fashion these days. The application of machine learning technology in trading or stock market is used so that the system automatically learns the complexity of the trade and improves its algorithms to assist with the best trading gift. In the last decade, there seemed to be a use of the wallet of traders, so that everyone could earn their profits. But, with the help of AI, one can perfectly analyze the underlying data points presented very quickly and accurately.

Using such data points, we can analyze current market trends and train high speed patterns, which are the two necessary elements generally used for smart trading. Using headlines from news channels and news sources, reviews from social media, and comments on other platforms, AI can analyze the action by performing sentiment analysis on that data. Machine learning usually stores the results and metrics that gave those results and can better analyze the stock market.

Data often helps to find a better solution, especially in probability-based and sentiment-based activities, such as stock trading. But to this day, financial engineers also believe that it is impossible for a machine, left to itself, to beat the stock market. With the rise of technology, incredibly powerful computers can process almost countless data points in a matter of minutes. This means that they are also very capable of detecting historical and replicating patterns for intelligent trading in the market which are often hidden from ordinary human investors. We humans are simply not able to process such data or see these patterns at the same rate as a technologically capable machine. AI can evaluate and analyze thousands of stocks in a matter of moments, and so this technology adds even more speed to trading. Today, every millisecond counts, and with AI as a means of automated trading, it’s a wonder. AI is already learning to continually improve on its own mistakes. It deploys automated trading assistant robots and is constantly working to improve its performance by refining programming and entering huge masses of new data.

What is Open AI Gym Anytrading?

AnyTrading is an Open Source collection of OpenAI Gym environments for reinforcement learning based trading algorithms. The trading algorithms are mainly implemented on the basis of two of the biggest markets present: FOREX and Stock. AnyTrading aims to provide Gym environments to improve and facilitate the process of developing and testing reinforcement learning based algorithms in the field of market trading. This is achieved by implementing it on three Gym environments: TradingEnv, ForexEnv and StocksEnv. AnyTrading can help you learn about stock market trends and perform powerful analysis, providing in-depth insights for data-driven decisions.

Getting started with the code

In this article, we will implement a reinforcement learning based market trading model, in which we will create a trading environment using OpenAI Gym AnyTrading. We will use historical GME price data, then train and evaluate our model using reinforcement learning agents and the gymnastics environment. The following code is partly inspired by a video tutorial on Gym Anytrading, the link of which can be found here.

Library installation

The first essential step would be to install the necessary library. To do this, you can run the following lines of code,

! pip install tensorflow-gpu == 1.15.0 tensorflow == 1.15.0 stable-baselines gym-anytrading gym

Stable-Baselines will provide us with the reinforcement learning algorithm and Gym Anytrading will provide us with our trading environment

Import dependencies

Now let’s install the required dependencies to create a basic framework for our model, and we’ll use the A2C reinforcement learning algorithm to build our market trading model.

# Importing Dependencies
import gym
import gym_anytrading
# Stable baselines - rl stuff
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C
# Processing libraries
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
Processing of our dataset

Now, with our pipeline setup, let’s load our GME Market data. You can download the dataset using the link here. You can also use other relevant data sets such as Bitcoin data to run this model.

#loading our dataset
df = pd.read_csv('/content/gmedata.csv')
#viewing first 5 columns
df.head()
#converting Date Column to DateTime Type
df['Date'] = pd.to_datetime(df['Date'])
df.dtypes

Go out :

Date      datetime64[ns]
Open             float64
High             float64
Low              float64
Close            float64
Volume            object
dtype: object

#setting the column as index
df.set_index('Date', inplace=True)
df.head()

We will now transmit the data and create our gym environment for our agent to train later.

See also
How Fujitsu uses artificial intelligence
#passing the data and creating our environment
env = gym.make('stocks-v0', df=df, frame_bound=(5,100), window_size=5)

Setting the window size parameter will specify how many previous price references our trading bot will have so that it can decide to place a trade.

Test our environment

Now with our model setup, let’s test our basic environment and deploy our reinforcement learning agent.

#running the test environment
state = env.reset()
while True: 
    action = env.action_space.sample()
    n_state, reward, done, info = env.step(action)
    if done: 
        print("info", info)
        break
        
plt.figure(figsize=(15,6))
plt.cla()
env.render_all()
plt.show()

As we can see, our agent RL bought and sold stocks at random. Our profit margin appears to be greater than 1, so we can determine that our bot has made us profit from the trades it has made. But these were random steps, now let’s properly train our model to get better trades.

Shaping our environment

Configure our environment to train our reinforcement learning agent,

#setting up our environment for training 
env_maker = lambda: gym.make('stocks-v0', df=df, frame_bound=(5,100), window_size=5)
env = DummyVecEnv([env_maker])

#Applying the Trading RL Algorithm
model = A2C('MlpLstmPolicy', env, verbose=1) 
 
#setting the learning timesteps
model.learn(total_timesteps=1000)
---------------------------------
| explained_variance | 0.0016   |
| fps                | 3        |
| nupdates           | 1        |
| policy_entropy     | 0.693    |
| total_timesteps    | 5        |
| value_loss         | 111      |
---------------------------------
---------------------------------
| explained_variance | -2.6e-05 |
| fps                | 182      |
| nupdates           | 100      |
| policy_entropy     | 0.693    |
| total_timesteps    | 500      |
| value_loss         | 2.2e+04  |
---------------------------------
---------------------------------
| explained_variance | 0.0274   |
| fps                | 244      |
| nupdates           | 200      |
| policy_entropy     | 0.693    |
| total_timesteps    | 1000     |
| value_loss         | 0.0663   |
#Setting up the Agent Environment
env = gym.make('stocks-v0', df=df, frame_bound=(90,110), window_size=5)
obs = env.reset()
while True: 
    obs = obs[np.newaxis, ...]
    action, _states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    if done:
        print("info", info)
        break


#Plotting our Model for Trained Trades
plt.figure(figsize=(15,6))
plt.cla()
env.render_all()
plt.show()

As we can see here, our skilled agent is now doing much better trades and a lot less random trades, giving us profit at the same time with a lot more awareness of when to buy and when to sell the stock.

End Notes

In this article, we have tried to understand how artificial intelligence can be applied to market trading to help leverage the art of buying and selling. We have also created a reinforcement learning model whereby our skilled agent can buy and sell stocks, reserving us profits simultaneously. The following implementation above can be found as a Colab notebook, accessible using the link here.

Good learning!

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Financial market

Political and economic data – main factors of financial market volatility

Chris Harmse

In a week of numerous political changes in South Africa, along with expectations of economic data and falling precious metal prices, stock prices on the JSE have moved a lot and remained volatile, while the rand s ‘is strongly depreciated on Friday.

The two main factors were Tito Mboweni’s resignation as finance minister, and the appointment of new finance minister, Enoch Godongwana, in his place and the release of the latest US employment data on Friday.

Godongwana’s appointment as the new finance minister will draw some criticism, given his experience as deputy economic development minister in 2012 and his resignation at the time after allegations of fraud. His resignation was due to outrage in government circles over his involvement in a company that allegedly defrauded workers at a R100 million garment factory out of their pension fund money. The financial markets are therefore to some extent skeptical of his appointment.

The rand lost about 40 cents against the dollar in the 24 hours after his appointment and traded late Friday night around R14.63 to the dollar. Against the pound, the currency traded on Friday alone, 21 cents lower on R20.29 and lost 25c against the euro, to trade at R17.21. This after the currency saw a strong rally in the first four days of the week, with the rand trading against the dollar at a time as strong as Rand 14.29.

Godongwana is a leading figure in the ANC on economic policy. He has headed the ANC’s Economic Transformation Committee for over a decade and served as Chairman of the Development Bank of Southern Africa.

Godongwana is seen as the one who frequently tries to persuade his colleagues to make pragmatic and market-friendly decisions. Therefore, we have to believe that the new Minister of Finance will not play partisan politics by favoring civil servants with unnecessary salary increases, the nationalization of the SA Reserve Bank and other big bailouts for state-owned enterprises.

Time will tell and financial markets and domestic and global investors will watch it closely. Dare we say it won’t abuse the gold super-cycle tax revenues from booming mining and agricultural exports.

In financial markets, gold and platinum prices fell during the week over fears that the Delta variant of the Covid-19 virus will continue to haunt countries in Asia and Europe. The price of gold has fallen from over $ 60 (R878) to $ 1,762 and the price of platinum from $ 64 to $ 975 an ounce. Instead, investors turned to the dollar and US stocks on Wall Street. This positive sentiment towards the United States strengthened on Friday after the release of better-than-expected employment data. The US unemployment rate was also lower than expected.

Last week, on the JSE, the all-stock index traded down 0.4%, while the Industrial 25 index lost 1.4%, following a massive sell-off by heavyweights Naspers, as well as stronger rand, most of the week.

Financials gained 5.1%, mainly due to the initial currency appreciation and trade in listed real estate up 2.6%. The Resources 10 index lost 1.9%. In the capital markets, investors played the card of caution and sold part of their bond holdings. The R186 short-term bond fell 0.5%, with the rate falling from 7.34% to 7.38%.

Next week, investors and analysts will focus on releasing South Africa’s manufacturing and mining production data for June.

Sacci will also release its latest Business Confidence Index.

In global markets, all attention will be on the announcement of the latest US inflation rate for July, as well as weekly unemployment data on Thursday.

Germany will release its trade balance and the UK will release its preliminary gross domestic product growth figure for the second quarter of 2012 and its trade balance for June.

Chris Harmse is the economist at CH Economics.

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Morningstar India’s National Fund Flows Report for Q1 FY22 provides an overview of estimated flows, asset trends and performance of equity and debt funds.

These mutual funds got the maximum flow during the June quarter.  Do you own any of them?


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Sbi 428.05 1.60 0.38
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Snapshot of the IPO

Equity Type Issue price Size of the problem Lot size Open problem Problem Close
View profile Initial Public Offering 1 2937.36 – 2998. 9 09-08 11-08
Nuvoco Vistas View profile Initial Public Offering 560 5000 – 5089.29 26 09-08 11-08
Aptus value See profile Initial Public Offering 346 2734.84 – 2790. 42 10-08 12-08
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FSN E-Commerce Ventures Limited (FSN.pdf)

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Glenmark’s life 720 06-08 752.00 748.20 3.92 748.00 3.89
Tatva Chintan 1083 07-29 2111.80 2310.25 113.32 2,009.00 85.50
Zomato 76 07-23 115.00 125.85 65.59 135.80 78.68
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Gretex Corporat See profile

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Financial market

Fiscal consolidation commitment keeps investors in financial markets

Investors’ position in the financial market remains unchanged

The position of investors in the financial market remains unchanged, as the government, through the mid-year budget review, has committed to return to fiscal consolidation. Investors, in their anticipation, assessed the economy’s worst-case scenario. However, the result for the first half of 2021 suggests a favorable budgetary situation, in the middle of the third wave of the coronavirus pandemic.

Provisional fiscal data from the 2021 Mid-Fiscal Policy Review revealed the government’s commitment to fiscal consolidation, as data from January to June 2021 show an overall budget deficit of $ 22.32 billion. GH ¢, or 5.1% of GDP, compared to a programmed target. of GH ¢ 22.73 billion, 5.2% of GDP.

Databank Research senior analyst Courage Kingsley Martey said in an interview with the B&FT that this indicates investors prefer to stay overweight Ghana Government Bonds (GHGBs) given that the market’s pre-budget stance is already assessed in a worse tax situation. results.

“Investors were not shaken by the mid-year budget review, as we saw no transactions or adverse reactions to the mid-year budget review. In fact, it appears that the general expectation was the worst-case scenario where the budget result for the first half of the year would be an unfavorable result, ”said Martey.

He added: “We have seen some large investors prefer to remain overweight Ghana Government Bonds (GHGBs) as the market’s pre-budget stance was already predicting a worse budget outcome, which did not exactly materialize from the start. ‘review presented. “

Republic Investment Managing Director Madeline Nettey also shared the same point of view in an interview with B&FT, stating; in general, there were not many changes before and after the mid-year budget reading, as the budget did not call for additional spending.

“I want to believe in part that this budget does not call for additional spending. Even more, we have seen that inflation is moving around the same parameters. So that in itself also did not push any reaction in any direction, nor to upward or downward.

“More often than not, investors react immediately to changes in the monetary policy rate. However, we do not anticipate any change in the TPM given the position and outlook for headline inflation. This should prevent the market from making any major changes, all of them. things being equal, ”she said.

Although the government raised the primary deficit target from 1.3 percent in the original budget to 2 percent in the revised budget, financing or borrowing needs have remained unchanged from the original plans of late.

Commenting on this, Mr Martey said: “This tells you that the government is signaling a commitment to fiscal consolidation despite the risk of a revenue shortfall. interest expense in order to leave borrowing requirements unchanged.

Finance Minister Ken Ofori Atta, in his presentation to Parliament, said the government remains fully committed to meeting the budget deficit target of 9.5% of gross domestic product (GDP) for this year, aimed at returning to the Fiscal Responsibility Act (FRA), by 2024.

During the period, the government embarked on a frontloading of its financing requirement, signaling a much lower financing requirement in the second half of the year.

“The market rightly saw this signal, so trading continued without major budget disruption. the second half of the year to partially mitigate any shortfall, ”said Mr. Martey.

The stock of public debt, as a percentage of GDP, rose from 76.1% at end-December 2020 to 77.1% of GDP at end-June 2021, including bailouts in the financial and energy sectors.


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Market trading

Malawi: stock exchange slows

A monthly market report released by the Malawi Stock Exchange (MSE) showed that trading slowed down on the local stock exchange in July, recording an average daily volume of 1,047,162 shares compared to 7,391,068 shares traded in June 2021.

This reflects an 85.83% drop in daily trading activity during the month.

The report indicates that the market traded a total of 21,990,398 shares for a total consideration of K994,831,445.53 in 298 transactions less than a total of 162,603,497 shares for a total consideration of K4,769,000 833.21 in 326 commercial transactions recorded in June.

However, the market recorded a positive return on the index, as evidenced by the upward movement of the Malawi All Share (Masi) index to 36,496.03 points against 35,144.56.63 points recorded in June.

“This gives a monthly return on the index of 3.85%. The price gains recorded by Illovo at 20.81%, Airtel at 9.9 percent, FMBCH at 8.66%, Limited press company at 8.34%, TNM, National Bank and Standard bank were enough to compensate for the price losses recorded by NITL at 15 percent, Former mutual to 4.11, NBS and FDH Bank resulting in an upward movement of the MASI, ”the statement said.

Stockbrokers Malawi Limited CEO Noel Kadzakumanja said the Masi won because it is driven by price changes on the counters and not by the volumes and values ​​traded.

“We should expect an increase in transactions as some of the big players come out of the shutdown period and the Covid situation is expected to improve,” Kadzakumanja said.

Alliance Stockbrokers Limited chief operating officer Thokozani Saulosi said in a separate interview that when overall prices rise over a period of time, Masi will rise.

He said the market exhibits seasonal patterns where activity is at will once companies’ financial statements are released, which acts as a trigger in the decision-making of fund managers who are the biggest investors in the market. Marlet.

“We should expect lower volumes and values ​​than in April and May. However, this is not exhaustive as some months activity could pick up and the market registers high values ​​and volumes, ”Saulosi said.

MSE operations manager Kelline Kanyangala said some of the decline in trade is indicative of the seasonality factor in business patterns that have been observed over time.

“Looking ahead, we hope that activity should pick up once most companies start reporting their half year financial results.

“However, we are also aware of the downside risk posed by the Covid pandemic which, we have observed, causes a slowdown in activity when positive cases increase,” Kanyangala said.


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