definitions

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Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or unfeasible to develop conventional 

algorithms to perform the needed tasks. 

 In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and will tend to become closer to the expected value as more trials are performed.The LLN is important because it guarantees stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the law only applies (as the name indicates) when a large number of observations is considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be "balanced" by the others (see the gambler's fallacy).

 The Kelly criterion is a mathematical formula relating to the long-term growth of capital developed by John L. Kelly, Jr. The formula was developed by Kelly while working at AT&T's Bell Laboratories. The formula is currently used by gamblers and investors for risk and money management purposes, to determine what percentage of their bankroll/capital should be used in each bet/trade to maximize long-term growth.The term is often also called the Kelly strategy, Kelly formula or Kelly bet, and the formula is as follows:Kelly % = W - {(1-W)}{R} where: Kelly% = Percent of investor's capital to put into a single trade W = Historical win percentage of trading systemR = Trader's historical win/loss ratio​
How to calculate the Kelly Criterion:There are two key components to the formula for the Kelly criterion: the winning probability factor (W) and the win/loss ratio (R). The winning probability is the probability a trade will have a positive return.The win/loss ratio is equal to the total positive trade amounts, divided by the total negative trading amounts. The result of the formula will tell investors what percentage of their total capital that they should apply to each investment.
What doe Kettly Criterion tell you? After being published in 1956, the Kelly criterion was picked up quickly by gamblers who were able to apply the formula to horse racing. It was not until later that the formula was applied to investing. More recently, the strategy has seen a renaissance, in response to claims legendary investors Warren Buffet and Bill Gross use a variant of the Kelly criterion.  

Bookkeeping is the recording of financial transactions, and is part of the process of accounting in business and other organizations. It involves preparing source documents for all transactions, operations, and other events of a business. Transactions include purchases, sales, receipts and payments by an individual person or an organization/corporation. There are several standard methods of bookkeeping, including the single-entry and double-entry bookkeeping systems. While these may be viewed as "real" bookkeeping, any process for recording financial transactions is a bookkeeping process.  Have you opened a new location, redesigned your shop, or added a new product or service? Don't keep it to yourself, let folks know.

Spread betting is any of various types of wagering on the outcome of an event where the pay-off is based on the accuracy of the wager, rather than a simple "win or lose" outcome, such as fixed-odds (or money-line) betting or parimutuel betting.A spread is a range of outcomes and the bet is whether the outcome will be above or below the spread. Spread betting has been a major growth market in the UK in recent years, with the number of gamblers heading towards one million. Financial spread betting can carry a high level of risk if there is no "stop". In the UK, spread betting is regulated by the Financial Conduct Authority rather than the Gambling Commission.

 Dutching or Dutch Betting, is a betting expression that means to back more than one horse or outcome rather than just bet on the one.  Before the betting exchanges emerged, dutch betting was used as a way of laying horses, by simply backing everything else in the race against the horse you wanted to 'lay'.Now it used as a definite strategy when covering more than one outcome or as in our case, more than one horse.  So let's say you have a nightmare big field Handicap like the six furlong Wokingham Stakes at Ascot and there are 20 to 30 runners. To give yourself more of a chance of backing the winner, you may decide to back more than one runner.  Bearing in mind the odds of the favourite in a race like this will probably be no lower than 5/1, you can cover a number of runners and still make a profit if one of them wins.   So if you select two horses, one at 11/1 and another at 8/1, both likely to still be near the front of the market here, there are three options available to you.Dutch Betting options.  1) Either split your stake and place the same amount on both horses, so for a £10 stake place £5 on each horse.  2) Stake more on one horse and the rest as a saver bet on the other, so that you get your stakes back if the one with the smaller stake wins.  3) Stake a different amount on each horse so that whichever horse wins you make the same profit.

 Money management refers to the processes of budgeting, saving, investing, spending, or otherwise overseeing the capital usage of an individual or group. The predominant use of the phrase in financial markets is that of an investment professional making investment decisions for large pools of funds, such as mutual funds or pension plans.Money management can also refer more narrowly to "investment management" and "portfolio management."

In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include: simple, and cumulative, or weighted forms (described below).Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next value in the subset.A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly. For example, it is often used in technical analysis of financial data, like stock prices, returns or trading volumes. It is also used in economics to examine gross domestic product, employment or other macroeconomic time series. Mathematically, a moving average is a type of convolution and so it can be viewed as an example of a low-pass filter used in signal processing. When used with non-time series data, a moving average filters higher frequency components without any specific connection to time, although typically some kind of ordering is implied. Viewed simplistically it can be regarded as smoothing the data.

Some further information about "technical analysis":

https://commons.wikimedia.org/wiki/Category:Technical_analysis