An investmentfonds wikipedia free fund also index tracker is a mutual fund or exchange-traded fund ETF designed to follow certain preset rules so that the fund can track a specified basket johann pfeiffer iforex underlying investments. Index funds may also have rules that screen for social and sustainable criteria. An index fund's rules of construction clearly identify the type of companies suitable for the fund. Additional index funds within these geographic markets may include indexes of companies that include rules based on company characteristics or factors, such as companies that are small, mid-sized, large, small value, large value, small growth, large growth, the level of gross profitability or investment capital, real estate, or indexes based on commodities and fixed-income. Companies are purchased and held within the index fund when they meet the specific index rules or parameters and are sold when they move outside of those rules or parameters. Think of an index fund as an investment utilizing rules-based investing.

This research also covers the contrarian rules of the three selected rules. The principles are same but the generated signals are opposite to the momentum. Thus, six kinds of rules are covered actually. The following descriptions include all details of each rule:. It is the basic rule of technical traders. The indicator shows whether market price change of a contract is positive or negative over a time period.

If the current price is higher or lower or equal than the price at a defined time point, the rule would show the buy or sell or keep nature signals. The principle is that technical traders trust the price movements will bring the same price movements as before. It depends on the difference between current and previous price. Also, based on the momentum rule, we introduce Contrarian Rule IMO which is the opposite rule to momentum. They have same principle but inverse execution: when the price change is positive or negative , the traders will sell or buy.

Moving Average Rule MA considers the weighting of all prices during a previously defined trading period. Through calculating average price over a specific period, trader can identify whether traders act transaction. If the current price is higher or lower or equal than the average price during the previous trading period, the rule indicate the buy or sell or keep nature signals.

We refer to the literature from Park and Irwin When we define a specific trading period, BO shows a buy signal if the last current price is the highest price and generates a sell signal if the last current price is the lowest price during the period. As the mention from Jackson and Ladley , the principle of BO is to utilise the local maximum and minimum price as the motivation of technical traders in order to implement their strategies. Before the first breakout, the indicator always stays equal to 0.

After the first breakout, if the price does not satisfy the condition of changing indicator, the indicator follows the last previous indicator. All the above technical trading rules need to be calculated and generated by time division data. Most previous research used the data with same time interval, such as daily data.

In other words, the data does not need to be modified smoothing because the same time interval is a kind of time series data and also it is the main feature of time division data. However, this paper utilises tick-by-tick data both data1 and data2 , which has the different time intervals for each record. Tick-by-tick data is the records of all transactions in the market. When one transaction happens, the data will add one record. Therefore, tick-by-tick data cannot be directly adopted to generate dummy signals of technical rules.

Even so, we use a general smoothing technique to transfer the tick-by-tick data to the time-series data in order to guarantee that there is only one price at each trading second. The aim is to use all the total market information to identify different trading rules.

Thus, we utilise data2, which includes all ticks for all contracts, to smooth in order to generate dummy trading signals under different technical rules. We refer to use a simple method to fill empty record on the time series. The time series time division data has been created in the last step so that we start completing the generation of dummy trading signals with different rules. Also, the main research contracts are 15 mentioned active contracts previously, thus we split 15 contracts as individual contracts to generate signals with 81, rules.

In each file, it contains a matrix, where the column indicates 81, rules and the row indicates the price movement of the contract after smoothing data. Because the amount of observations of 15 contracts is not same, the size of matrix is not same. Thus, the columns rules are fixed as 81,, and the rows are between 1,, and 3,, Then, we have produced and introduced data3, which is very important to explain the effect of all selected technical rules.

In each type, members should have generally similar strategies. In next section, we describe our method to link data1 and data3 and also show adopted classification method, which is K-means clustering algorithm. There is a connection between data1 and data3.

In data3, as we introduced, it is time series data which means there is only one record dummy signal of one rule for all possible trading time. As above, the selected research sample covers 81 pure technical traders, and they have different amounts of transactions observations or actions. Next, we insert dummy signals of all rules from data3 into each individual dataset with considering same time points.

In other words, the originally individual dataset only include two columns—occurred time points and real actions, and now, 81, columns are added in the reconstituted dataset. Each column indicates the dummy trading signals of one specific technical rule.

Thus, each of 81 matrices just provides one notice—the similarity. We put a short sample as below. Thus, we classify 81 traders in different groups based on the above mentioned similarity. Cluster analysis Footnote 4 is used to classify many objects in different groups clusters with same features. In each group, there is a centroid, and all members have similar characteristics or coordinates to the centroid. Thus, we tend to adopt this method to group technical traders. There are various clustering algorithm.

In statistical analysis, clustering analysis generally put all observations in a multi-dimensional space, and each observation becomes a point with n-dimensional attributes in the space if the space has n dimensions. Based on the distance between each point and centroid, the algorithm select nearby points to each centroid as a group, which is centroid-based clustering.

In my research, the similarities of each trader to each rule are seen as attributes in the clustering space, so that clustering algorithm is easy and sensible to realise classification of technical traders. The logistic design is to put all 81 traders in the space: in other words, 81 points would be grouped. Therefore, the above mentioned space is an 81,dimensional space for clustering. Centroid-based clustering generally has two popular ways.

Each point is one centroid at the beginning of clustering process. Then, the algorithm continuously merges close centroids to create a new centroid before finding the optimal number of centroid. After that, the process classifies all points in the space with optimal centroids. The other popular way of centroid-based clustering is k-means clustering, which we adopt in this work.

The main principle of k-means clustering is to partition all observations in the space into k groups. The clustering results also depend on the optimal distance, which is the least mean of all distance between member points and their individual centroids.

The variety of distance can be appointed, such as city block and hamming distance. In this research, we utilise Matlab Rb to realise k-means clustering because Matlab has standard procedure package of k-means. Also, we adopt the default distance—squared Euclidean distance SED of this automatic procedure.

After identifying k, the algorithm starts stochastically set k centroids in the space. In the assignment step, k-means repeatedly moves the centroids until finding the optimal distance as above description. Then, the clustering is finished and we can get a sensible classification of traders. There is one significant problem is that how to decide the number of k. There are many methods discussing in clustering area.

The principle of this method is that, with increasing number of k setting, in each cluster, the average sum of distance between each point and their centroids and average sum of variance of distance in each group will be decreasing.

When these two sums are close to 0 or at a lowest level in the dimension, they will not have a big change. Then, the corresponding number of k should be the decided and optimal k in the algorithm. Although, this is a roughly estimated method for k clusters, we designed three projects three samples to prove the correct number of k. Where, the x-axis is the number of k. We make 23 times of clustering with setting k equal 2 to The y-axis is the value of average sum of distance and variance.

Thus in project one, all 81 traders should be divided into 11 groups with 81, attributes rules. It initially proves the number of classifications in the research, and it covers all investigated rules. However, this is a biased estimation, and the dimensional-space is very complex. Thus, we designed other two projects to support the clustering results.

The principle is that we reduce the dimension of attributes in the space—We remove a lot of rules from the original 81, rules with two different criterions. We can see the variation in Fig. Similar to P2, but it refers to the previous research. The Fig. Then, the space becomes smaller and dimensional-space but the common features have not been changed because the selected rules are constructed by a standard and regular interval.

It looks like the general sampling estimation. If the clustering results of this two small space are same as the original one, 11 should be recognised as the correct number of k. The above two graphs show the clustering results of project two and three. X-axis and y-axis have the same explanations of P1. It is important to note that it looks like that 9 or 10 would be the clustering results due to the dimension setting. However, there is still a length to 0 when cluster is 9 or When cluster adopting 11, the length is really close to 0 and able to be acceptable.

We make 20 operations of P2 for the number of k equals 2—20 and 15 operations of P3 for the number of clusters equals 2— Also, due to the k-means algorithm randomly set the centroids, we run the program ten times for each project in order to get the relatively optimal clusters and keep a low level of variance. In the graph, it is very clearly that after grouping 81 traders into 11 groups, the ASD and ASV become stationary, which is the best evidence to support the clustering results of project one.

Fortunately, no matter which project, some members are always in one group, in other words, the clustering of project one is successful. Therefore, all selected 81 pure technical traders can be classified in 11 groups with 81, attributes technical trading rules. In order to robustness check the clustering results, we split 81 traders into two groups: trader 1—40 as the first group and trader 41—81 as the other group.

Then, we operate k-means clustering algorithm to each group with 81, rules. If the clustering results is similar as P1: around 11 centroids, we can confirm the results in nature. As the same principle above, we can confirm 11 or around this number should be the correct number of centroids in the space.

It is also displayed in Fig. After clustering process of project one, 81 pure technical traders are divided into 11 groups. Thus, the 11 centroids summarise characteristics of members in their individual set. Using this matrix, we start further exploring trading strategies for each group.

If the similarity of the rule is higher, the rule is much more related to real actions. In the following steps, we select higher similarity rules and return to the dataset of dummy signals to draw out dummy signals according to the specific rules for every trader. The selected technical rules cover six kinds of rules as mentioned before. The corner mark j of S is label of different traders from 1 to In each kind of rule, it includes 13, rules.

The independent variables are all dummy signals with all 81, rules. Thus, we make 81 regressions with this model, and in each model, the total observations are equal to the total transaction records for each individual trader. However, this is the original investigated model. It cannot be realised due to the huge similar signals with different rules. In other words, the problem of collinearity happens.

The same or correlated variables possibly exist in total 81, rules after experiment, same rules exist. The general methods of this question are to remove the same or correlated variables in the model. However, this is not advisable in this research. For example, if the vector of BO22 is equal to MA16 for trader 1, we do not know which rule we need to remove. So, we stop investigating all 81, rules and contact clustering results to seek some main significant rules with Top Six Project.

Thus, 11 groups with top six highest rules are filtered. In each group, the six rules construct the key strategies of members. Therefore, the original multiple-regression model can be transformed and simplified as:. The following independent variables only content six specific rules based on the results of clustering.

The corner mark g is from 1 to 11 which label the different group, j is from 1 to 81 which label the different traders, and r is the mark of rules which is selected from 1 to 13, The amounts of regressions are 81 and they actually divided into 11 groups as clustering. The first and second columns show the code of group and amount of members in the group. The third column shows the six specific rules with highest similarity in each group.

Then, the following 4 columns show how many traders are affected by the six rules and how many are not, and the probability of total traders. For instance, the second big row actually includes 19 regressions for 19 traders in group 1. The rule of BO13 has effect to 17 traders in this group, and the occupation is Traders in group 2 utilise ma, mo, and ibo in their strategy absolutely.

In group 4, most traders adopt bo1 and ma18 rules. All traders utilise bo8, ma41, and mo in their strategy. There is only one trader in this group. But, he is very interesting because he also is single trader in one group of project 2 and 3.

The size of trader is small in this group so that the adopted rules are not clear. The rule of ibl94 is adopted for this group of traders. This also is a small group so that the indication is not very clear. These 10 traders utilise ma65 and tend to adopt ibo and mo92 in their strategy. Most traders in this group adopt bo40, ma, and mo in their strategy.

The five traders in the last group use the ima, imo, and bo in their strategy. The results are according to the coordinates of clusters in each group. Hence, the strategies set can prefer the features of technical traders. This is an empirical research on technical trading strategies.

The contribution of this research is trying to indicate and disclose technical trading behaviour in Chinese rebar futures market and create a new method to capture technical trading strategies. We selected rebar futures contracts, which could be recognized as a representative commodity futures in Chinese futures market, as the underlying asset to investigate.

According to the unique feature of the dataset, traders have their own identification. The top most active traders were the main research object since they were more likely to be the technical traders and employ program trading. We chose five related macroeconomic indexes to rebar market as the filter factor by using a simple multiple-regression model to filter technical traders. We selected only 81 technical traders from 15 most active contracts in my dataset for investigation. Based on the similarity matrix, we adopted k-means clustering algorithm to classify these 81 traders.

The clustering results showed that they could be divided into 11 groups with different technical strategies. The results indicated that most members, in the different groups, had to have one or more significant technical rules to their real action. More details are displayed in Sect. We will continuously improve our dataset in two part: cover all commodities in the market and bring more types of technical rules in the system. Then, that will be more significant that we will test whether the summarized strategies are profitable in each group of technicalists.

There would be some interesting things appearing in our further works. Net position means investor will sell or buy how many contracts of commodity on delivery day after one transaction. In the futures market, long means investors expect to buy futures contracts, and short means investors expect to sell futures contracts. Investors can either take long or short position for their open position. Offset position is the opposite act to open position Hull Average sum of distance ASD : After clustering, algorithm captures the sum of distance between every point and their attributive cluster P to C distance in k groups, and then gets average of k sums.

Less variance implies more stable and optimal members in each group. Bagehot, W. The only game in town. Financial Analysts Journal , 27 2 , 12— Biais, B. Equilibrium fast trading. Journal of Financial Economics , 2 , — Bodie, Z. Commodity futures as a hedge against inflation. The Journal of Portfolio Management , 9 3 , 12— Risk and return in commodity futures.

Financial Analysts Journal , 36 3 , 27— Boswijk, P. Success and failure of technical trading strategies in the cocoa futures market. Brock, W. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance , 47 5 , — Brogaard, J. High-frequency trading and the execution costs of institutional investors. Financial Review , 49 2 , — High-frequency trading and price discovery.

The Review of Financial Studies , 27 8 , — Carrion, A. Very fast money: High-frequency trading on the nasdaq. Journal of Financial Markets , 16 4 , — Chan, K. Profitability of momentum stragegies in the international equity markets. Journal of Financial and Quantitative Analysis , 35 2 , — Conrad, J. An anatomy of trading strategies.

Review of Financial studies , 11 3 , — Cornell, W. The efficiency of the market for foreign exchange under floating exchange rates. The Review of Economics and Statistics , 60 1 , — De Long, J. Noise trader risk in financial markets. Journal of Political Economy , 98 4 , — The survival of noise traders in financial markets. The Journal of Business , 64 1 , 1— Donchian, R. Commodities: High finance in copper. Financial Analysts Journal , 16 6 , — Erb, C.

The strategic and tactical value of commodity futures. Financial Analysts Journal , 62 2 , 69— Faber, M. A quantitative approach to tactical asset allocation. The Journal of Wealth Management , 9 4 , 69— Fabozzi, F. The handbook of commodity investing. London: Wiley. Google Scholar. Fama, E. Efficient capital markets: A review of theory and empirical work. The Journal of Finance , 25 2 , — Fifield, S.

The performance of moving average rules in emerging stock markets. Applied Financial Economics , 18 19 , — Foucault, T. News trading and speed. The Journal of Finance , 71 1 , — Limit order book as a market for liquidity. Review of Financial Studies , 18 4 , — Franke, R. Structural stochastic volatility in asset pricing dynamics: Estimation and model contest.

Journal of Economic Dynamics and Control , 36 8 , — Gehrig, T. Extended evidence on the use of technical analysis in foreign exchange. International Journal of Finance and Economics , 11 4 , — Gencay, R. Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules.

Journal of International Economics , 47 1 , 91— Technical trading rules and the size of the risk premium in security returns. Gorton, G. Facts and fantasies about commodity futures. Financial Analysts Journal , 62 2 , 47— Hull, J. Options, futures, and other derivatives. Irwin, S. A performance comparison of a technical trading system with arima models for soybean complex prices. Advances in Investment Analysis and Portfolio Management , 4 , — Jackson, A.

Market ecologies: The interaction and profitability of technical trading strategies. International Review of Financial Analysis , 47 , — Jegadeesh, N. Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance , 48 1 , 65— Jiang, G. High frequency trading in the US treasury market: Evidence around macroeconomic news announcements.

Kozhan, R. Execution risk in high-frequency arbitrage. Management Science , 58 11 , — Levich, R. The significance of technical trading-rule profits in the foreign exchange market: A bootstrap approach. Journal of International Money and Finance , 12 5 , — Lo, A.

When are contrarian profits due to stock market overreaction? Review of Financial studies , 3 2 , — Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance , 55 4 , — Lucke, B. Are technical trading rules profitable?

Evidence for head-and-shoulder rules. Applied Economics , 35 1 , 33— Lui, Y. The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence. Journal of International Money and Finance , 17 3 , — Lukac, L. A test of futures market disequilibrium using twelve different technical trading systems.

Applied Economics , 20 5 , — Maimon, O. Data mining and knowledge discovery handbook. Berlin: Springer. Marshall, B. Does intraday technical analysis in the us equity market have value? Journal of Empirical Finance , 15 2 , — Miffre, J. Momentum strategies in commodity futures markets.

Journal of Banking and Finance , 31 6 , — Neely, C. The temporal pattern of trading rule returns and exchange rate intervention: Intervention does not generate technical trading profits. Journal of International Economics , 58 1 , — Intraday technical trading in the foreign exchange market. Journal of International Money and Finance , 22 2 , — The adaptive markets hypothesis: Evidence from the foreign exchange market.

Now, after working with hundreds of businesses all over the UK, we understand how to customise our service to your specific needs. Together, we can help you take control of the fundamentals and give you back the space you need to thrive.

Are you ready to reshape your business performance? Welcome to Futurform — an exceptional team of local talent. Reach out today. Ethical business is integral to Futurform. We are constantly working to reduce our environmental footprint, refine our supply chain and deliver positive social impact. Not just for us, but for our all our stakeholders and the wider community, too. But at Futurform we have a formula that will let your budget stretch.

We call it smarter spending and it starts with you. What would it mean if you could streamline procurement? If you understood what your office needs to be truly effective, and where you could find the best value on all your office products? News Contact Us Customer Login. Fulfilment Large Format Print Design. Reputation is earned. Our Story Get In Touch.

Our one-to-one service is designed to meet your unique needs. Say hello to rapid delivery, a simple ordering dashboard and on-call support that make procurement easy. You can also benefit from simple purchasing audits and transparent reports that put you back in control. That means you can save both money and the headache.

Ready to browse a truly comprehensive portfolio of modern office supplies? Our doors are open. Take a look at our extensive range and see what we can do for you. Cost reduction Read more. Toner Recycling Read more.

News Contact Us Customer Login. Fulfilment Large Format Print Design. Office Supplies. Otherwise too, these days the demand for scented and therapeutic candles is also on the rise with many restaurants, households, and hotels using them to create an ambience. The candle-making business can be initiated from home with low investment of approximately Rs 20,Rs 30, The raw materials used to start the business include wax, wick, moulds, thread, aroma oils, and more.

Apart from the major raw materials, you also need to have some candle-making equipment. This includes a melting pot, thermometer, pour pot, weighing scale, hammer, and an oven to melt wax. Pickles are a traditional food item in India and are extremely popular. You will find at least one variant of pickle in every Indian household. Thus, if you want to start small, a pickle business is a safe and easy option. You can start this business at your home with a small capital of approximately Rs 20,Rs 25, Agarbattis are used in most Indian households, and their popularity and demand picks up during the festive season.

Their exports have also grown, on the back of the rising popularity of meditation in other countries and the associated use of agarbattis. The first step of small-scale manufacturing of agarbattis involves buying bamboo sticks and essential oils with fragrances such as sandalwood, jasmine, rose, champa, etc.

The sticks are coated with the oils, and dried. Automatic and semi-automatic agarbatti-making machines, costing upwards of Rs 50,, can be used for bulk production. Buttons are one of the most essential trimmings used in the garment industry and have huge market potential. From plastic to fabric and steel buttons, there are various categories in this niche that you can select depending upon your choice of business.

You can either rent out a space or start at home with a basic investment of approximately Rs 30,Rs 40, Lace is commonly used in garments and for craft work. It is a traditional form of business and can easily be started at home. With emerging fashion trends, the demand for different kinds of laces has increased. Laces are also exported to various countries, which makes this a good option for those who want to start small.

Laces can be designed manually, through bobby machines, or fully computerised machines — after you decide on the scale of operations. You can start this business with a low investment of approximately Rs 25,Rs 50, India is the second-largest producer of footwear after China. The shoes the country manufactures can be segregated into categories like sports, formal, casual, and others.

The demand for shoelaces is high as well, and manufacturing shoelaces has become a lucrative small business idea. Shoelaces are manufactured by weaving a band and fastening the aglet the hardened end of the lace. The simple, woven band is usually made from cotton, polyester, nylon, polypropylene, etc, and the aglet is made of plastic.

Apart from the material for the lace and aglet, shoe lace braiding machines are also required. They can weave several metres of lace per minute, after which acetone can be used to fasten the aglet to the woven band. You can start this business with a small investment of approximately Rs 25, depending upon the kind of machinery you want to deploy. Everyone screams for ice cream, one of the most popular desserts today. The increasing consumption of ice cream has led to a rise in the demand for ice cream cones.

Therefore, if you want to start small, this idea could be a profitable business option. You can start an ice cream cone manufacturing unit in a small space by investing approximately Rs 1 lakh to Rs 1. However, if you want to operate on a larger scale with high capacity machinery, the investment cost goes a little higher.

When it comes to chocolate consumption, India is on top of the chart. Be it sweet or bitter, chocolate is a mood lifter and stress buster. According to Mintel, sales of chocolate confectionery in retail markets grew by 13 percent between and in India. You need to develop a product line to start off. An approximate capital of Rs 40,Rs 50, will be required to purchase raw materials and packaging.

However, if you want to deploy a piece of machinery for a larger production scale, the cost may rise up to Rs 2 lakh-Rs 3 lakh. Your volume production will be easier with mixing, cooking, and cooling equipment. Select the type of equipment to fit the scale of your operation. The cotton buds market is being driven by growing per capita expenditure of consumers, increasing awareness of hygiene, rising population, etc.

The raw material then goes into the automatic cotton bud-making machines, many of which package the products too. The machines are available as per the quality and output requirements of the entrepreneur. The cotton buds manufacturing business can be started with an investment of Rs 20,Rs 40, The thin, crispy food — fried or roasted - is a common accompaniment to most meals across India.

Papads are mandatory at several occasions, functions, celebrations, and parties, which means demand is always high. The manufacturing process is relatively simple once basic ingredients such as wheat flour, spices, and oil are sourced. The large-scale papad manufacturing industry is highly competitive, but entrepreneurs can start with a small investment of approximately Rs 30,Rs 40, and sell to local department stores.

Entrepreneurs can also experiment with flours made from lentils, chickpeas, rice, tapioca, etc to differentiate their offerings from others. Noodles , especially the instant variety, are a popular snack in India across rural and urban markets. The noodle manufacturing process is simple and requires basic ingredients such as wheat flour, salt, sugar, starch, spices, vegetable oils, etc. Both semi-automatic and fully automatic noodle-making machines are available in the market.

The process of making noodles involves blending flour, starch, and sodium bicarbonate, mixing the dough, and passing it through the machine. The noodles are cut into the desired shape and size, dried, and packaged. Low capacity noodle-making machines cost over Rs 40, while premium ones cost anywhere upwards of Rs 1.

Disposable food-grade plates and cups are used extensively in India during events, functions, picnics, etc. They are also used on a large scale by street vendors and hawkers. As they are widely used and have been inexpensive to make for a long time, the market has matured. They are usually made of paper, which has emerged as an alternative to plastic, steel, glass, etc.

To make paper plates and cups, paper can be acquired from local scrap shops at low rates. The major part of the investment goes into acquiring disposable plate-making machines. They cost over Rs 50,, depending on their capacity. With the world moving to ban plastic, a jute bag manufacturing business is a good choice.

The filtering model is divided into two parts: the first part identify the fundamental relations between individual trading volume and market price transaction price and individual net position. The regression model is as below:. The total market position is invoked by market data and based on same time points in both transaction and market data. Then, the variable of position is the individual net position variation tendency. We take the logarithm for these three variables in order to reduce the number size and decline the effect of heteroscedasticity.

These first two items on the right hand can show the fundamental relationship between individual trading volume and two controlled factors price and position. For the following items, d is the dummy setting for different traders which depend on the size of research sample can be set from 1 to 19, to identify different traders.

The next group of variables describe announcement time of the above five macroeconomic variables. It is important to note that we do not use the real public value of macroeconomic news announcement. We only utilize the announcement date of each index to create announcement time-variation series.

Therefore, the setting of T is the time changing trend between monthly or quarterly announcement and next announcement time of each macroeconomic index. This performance is used to identify and disclose whether the trader may consider the macroeconomic information of these five indexes with the public time of indexes pass by. Pure technical traders ignore all other external elements and only focus on previous price, in other words, the relationship between this five variables and trading activity trading volumes is insignificant for each pure technicalist traders.

The five relevant indices cannot influence decided trading volume of pure technical traders. Relatively, Chinese scheduled news and macroeconomic index publications can influence fundamentalist behaviours more efficiently, thus we selected these news rather than related prices, such as the price of steel and iron ore.

In attention, the main function of this regression is based on the regression results of this five macroeconomic timing variables, which can indicate who are technical traders. Also, in order to identify whether investors tend to buy or sell, we split the data into long and short two groups. The working sample is huge, so that the investigation is divided into two parts.

The first part is working on total sample through all records. These investors are the most active traders, who have the most transaction records, in my sample. They seem to be using algorithm to execute their technical strategies at a high frequency level. Because they are the most active traders, they should have significance to investigate and summarise the total sample of technical traders.

In addition, these top traders are organised according by the amount of their records. And, NO. This status is also consistent with real situation that individual investors hold most amounts of market participants. For the second part, it is the research on each single active futures contract. Since, rebar futures contract started on March 27th , according to the situation of trading volume, market position, and trading amount, we find only September, October, November, and December contracts in , and January, May, and October congtract in to can be defined as relatively active contracts.

This is caused by the seasonal economic cycle reason in China. They are surveyed in the second part of each single futures contract. The method is same as the first part. But, the investigated active traders increased from to because the decline of sample size. These investors are the top most trading people for each single rebar futures contract individually.

Meanwhile, we have also made a secondary task. After statistics, there are about 50 traders recognizing as pure technical traders in each contract, who both long and short do not have significance between macroeconomic indexes and their trading volume. The pure technical traders are selected by the filter model. However, some of them own fewer records in the sample of data1 less observations. Thus, we select the research sample of traders who satisfy two conditions: 1, the traders must be pure technical traders who have been filtered.

Only few traders has amount of trades between and so that we set as the threshold to keep high frequency traders, who are more likely to use technical program trading. Therefore, we select traders from top most active traders in each main futures contract. Some of them appear and can be selected in different contract, but we only choose one to symbolise this special traders.

For instance, if trader is identified as technical trader in two contracts, we only choose one contract as his research sample. After statistics, we choose 81 traders from each of 15 main contracts into the research sample. They are pure technical traders and have transaction records between and All the following research is based on these 81 traders. Certainly, Technical Traders only focus on the historical price chart.

They use the historical data to design a lot of different technical trading rules in order to execute their trading strategies. We select three kinds of popular technical trading rules as the bench mark of pure technical traders to investigate their behaviours.

Regard technical trading strategies, this research only selects three popular classes of technical trading rules Momentum, Moving Average, and Trading Range Breakout. The signals of different rules are generated by the time division data. This research also covers the contrarian rules of the three selected rules. The principles are same but the generated signals are opposite to the momentum. Thus, six kinds of rules are covered actually.

The following descriptions include all details of each rule:. It is the basic rule of technical traders. The indicator shows whether market price change of a contract is positive or negative over a time period. If the current price is higher or lower or equal than the price at a defined time point, the rule would show the buy or sell or keep nature signals. The principle is that technical traders trust the price movements will bring the same price movements as before.

It depends on the difference between current and previous price. Also, based on the momentum rule, we introduce Contrarian Rule IMO which is the opposite rule to momentum. They have same principle but inverse execution: when the price change is positive or negative , the traders will sell or buy. Moving Average Rule MA considers the weighting of all prices during a previously defined trading period. Through calculating average price over a specific period, trader can identify whether traders act transaction.

If the current price is higher or lower or equal than the average price during the previous trading period, the rule indicate the buy or sell or keep nature signals. We refer to the literature from Park and Irwin When we define a specific trading period, BO shows a buy signal if the last current price is the highest price and generates a sell signal if the last current price is the lowest price during the period. As the mention from Jackson and Ladley , the principle of BO is to utilise the local maximum and minimum price as the motivation of technical traders in order to implement their strategies.

Before the first breakout, the indicator always stays equal to 0. After the first breakout, if the price does not satisfy the condition of changing indicator, the indicator follows the last previous indicator. All the above technical trading rules need to be calculated and generated by time division data.

Most previous research used the data with same time interval, such as daily data. In other words, the data does not need to be modified smoothing because the same time interval is a kind of time series data and also it is the main feature of time division data. However, this paper utilises tick-by-tick data both data1 and data2 , which has the different time intervals for each record. Tick-by-tick data is the records of all transactions in the market. When one transaction happens, the data will add one record.

Therefore, tick-by-tick data cannot be directly adopted to generate dummy signals of technical rules. Even so, we use a general smoothing technique to transfer the tick-by-tick data to the time-series data in order to guarantee that there is only one price at each trading second.

The aim is to use all the total market information to identify different trading rules. Thus, we utilise data2, which includes all ticks for all contracts, to smooth in order to generate dummy trading signals under different technical rules. We refer to use a simple method to fill empty record on the time series. The time series time division data has been created in the last step so that we start completing the generation of dummy trading signals with different rules.

Also, the main research contracts are 15 mentioned active contracts previously, thus we split 15 contracts as individual contracts to generate signals with 81, rules. In each file, it contains a matrix, where the column indicates 81, rules and the row indicates the price movement of the contract after smoothing data. Because the amount of observations of 15 contracts is not same, the size of matrix is not same. Thus, the columns rules are fixed as 81,, and the rows are between 1,, and 3,, Then, we have produced and introduced data3, which is very important to explain the effect of all selected technical rules.

In each type, members should have generally similar strategies. In next section, we describe our method to link data1 and data3 and also show adopted classification method, which is K-means clustering algorithm. There is a connection between data1 and data3. In data3, as we introduced, it is time series data which means there is only one record dummy signal of one rule for all possible trading time.

As above, the selected research sample covers 81 pure technical traders, and they have different amounts of transactions observations or actions. Next, we insert dummy signals of all rules from data3 into each individual dataset with considering same time points.

In other words, the originally individual dataset only include two columns—occurred time points and real actions, and now, 81, columns are added in the reconstituted dataset. Each column indicates the dummy trading signals of one specific technical rule. Thus, each of 81 matrices just provides one notice—the similarity. We put a short sample as below. Thus, we classify 81 traders in different groups based on the above mentioned similarity.

Cluster analysis Footnote 4 is used to classify many objects in different groups clusters with same features. In each group, there is a centroid, and all members have similar characteristics or coordinates to the centroid. Thus, we tend to adopt this method to group technical traders. There are various clustering algorithm. In statistical analysis, clustering analysis generally put all observations in a multi-dimensional space, and each observation becomes a point with n-dimensional attributes in the space if the space has n dimensions.

Based on the distance between each point and centroid, the algorithm select nearby points to each centroid as a group, which is centroid-based clustering. In my research, the similarities of each trader to each rule are seen as attributes in the clustering space, so that clustering algorithm is easy and sensible to realise classification of technical traders.

The logistic design is to put all 81 traders in the space: in other words, 81 points would be grouped. Therefore, the above mentioned space is an 81,dimensional space for clustering. Centroid-based clustering generally has two popular ways.

Each point is one centroid at the beginning of clustering process. Then, the algorithm continuously merges close centroids to create a new centroid before finding the optimal number of centroid. After that, the process classifies all points in the space with optimal centroids.

The other popular way of centroid-based clustering is k-means clustering, which we adopt in this work. The main principle of k-means clustering is to partition all observations in the space into k groups. The clustering results also depend on the optimal distance, which is the least mean of all distance between member points and their individual centroids.

The variety of distance can be appointed, such as city block and hamming distance. In this research, we utilise Matlab Rb to realise k-means clustering because Matlab has standard procedure package of k-means. Also, we adopt the default distance—squared Euclidean distance SED of this automatic procedure. After identifying k, the algorithm starts stochastically set k centroids in the space.

In the assignment step, k-means repeatedly moves the centroids until finding the optimal distance as above description. Then, the clustering is finished and we can get a sensible classification of traders. There is one significant problem is that how to decide the number of k.

There are many methods discussing in clustering area. The principle of this method is that, with increasing number of k setting, in each cluster, the average sum of distance between each point and their centroids and average sum of variance of distance in each group will be decreasing.

When these two sums are close to 0 or at a lowest level in the dimension, they will not have a big change. Then, the corresponding number of k should be the decided and optimal k in the algorithm. Although, this is a roughly estimated method for k clusters, we designed three projects three samples to prove the correct number of k.

Where, the x-axis is the number of k. We make 23 times of clustering with setting k equal 2 to The y-axis is the value of average sum of distance and variance. Thus in project one, all 81 traders should be divided into 11 groups with 81, attributes rules. It initially proves the number of classifications in the research, and it covers all investigated rules. However, this is a biased estimation, and the dimensional-space is very complex.

Thus, we designed other two projects to support the clustering results. The principle is that we reduce the dimension of attributes in the space—We remove a lot of rules from the original 81, rules with two different criterions. We can see the variation in Fig. Similar to P2, but it refers to the previous research. The Fig. Then, the space becomes smaller and dimensional-space but the common features have not been changed because the selected rules are constructed by a standard and regular interval.

It looks like the general sampling estimation. If the clustering results of this two small space are same as the original one, 11 should be recognised as the correct number of k. The above two graphs show the clustering results of project two and three.

X-axis and y-axis have the same explanations of P1. It is important to note that it looks like that 9 or 10 would be the clustering results due to the dimension setting. However, there is still a length to 0 when cluster is 9 or When cluster adopting 11, the length is really close to 0 and able to be acceptable.

We make 20 operations of P2 for the number of k equals 2—20 and 15 operations of P3 for the number of clusters equals 2— Also, due to the k-means algorithm randomly set the centroids, we run the program ten times for each project in order to get the relatively optimal clusters and keep a low level of variance.

In the graph, it is very clearly that after grouping 81 traders into 11 groups, the ASD and ASV become stationary, which is the best evidence to support the clustering results of project one. Fortunately, no matter which project, some members are always in one group, in other words, the clustering of project one is successful. Therefore, all selected 81 pure technical traders can be classified in 11 groups with 81, attributes technical trading rules.

In order to robustness check the clustering results, we split 81 traders into two groups: trader 1—40 as the first group and trader 41—81 as the other group. Then, we operate k-means clustering algorithm to each group with 81, rules. If the clustering results is similar as P1: around 11 centroids, we can confirm the results in nature. As the same principle above, we can confirm 11 or around this number should be the correct number of centroids in the space. It is also displayed in Fig. After clustering process of project one, 81 pure technical traders are divided into 11 groups.

Thus, the 11 centroids summarise characteristics of members in their individual set. Using this matrix, we start further exploring trading strategies for each group. If the similarity of the rule is higher, the rule is much more related to real actions. In the following steps, we select higher similarity rules and return to the dataset of dummy signals to draw out dummy signals according to the specific rules for every trader.

The selected technical rules cover six kinds of rules as mentioned before. The corner mark j of S is label of different traders from 1 to In each kind of rule, it includes 13, rules. The independent variables are all dummy signals with all 81, rules. Thus, we make 81 regressions with this model, and in each model, the total observations are equal to the total transaction records for each individual trader. However, this is the original investigated model. It cannot be realised due to the huge similar signals with different rules.

In other words, the problem of collinearity happens. The same or correlated variables possibly exist in total 81, rules after experiment, same rules exist. The general methods of this question are to remove the same or correlated variables in the model. However, this is not advisable in this research. For example, if the vector of BO22 is equal to MA16 for trader 1, we do not know which rule we need to remove.

So, we stop investigating all 81, rules and contact clustering results to seek some main significant rules with Top Six Project. Thus, 11 groups with top six highest rules are filtered. In each group, the six rules construct the key strategies of members. Therefore, the original multiple-regression model can be transformed and simplified as:. The following independent variables only content six specific rules based on the results of clustering.

The corner mark g is from 1 to 11 which label the different group, j is from 1 to 81 which label the different traders, and r is the mark of rules which is selected from 1 to 13, The amounts of regressions are 81 and they actually divided into 11 groups as clustering.

The first and second columns show the code of group and amount of members in the group. The third column shows the six specific rules with highest similarity in each group. Then, the following 4 columns show how many traders are affected by the six rules and how many are not, and the probability of total traders.

For instance, the second big row actually includes 19 regressions for 19 traders in group 1. The rule of BO13 has effect to 17 traders in this group, and the occupation is Traders in group 2 utilise ma, mo, and ibo in their strategy absolutely. In group 4, most traders adopt bo1 and ma18 rules. All traders utilise bo8, ma41, and mo in their strategy. There is only one trader in this group. But, he is very interesting because he also is single trader in one group of project 2 and 3.

The size of trader is small in this group so that the adopted rules are not clear. The rule of ibl94 is adopted for this group of traders. This also is a small group so that the indication is not very clear. These 10 traders utilise ma65 and tend to adopt ibo and mo92 in their strategy. Most traders in this group adopt bo40, ma, and mo in their strategy. The five traders in the last group use the ima, imo, and bo in their strategy.

The results are according to the coordinates of clusters in each group. Hence, the strategies set can prefer the features of technical traders. This is an empirical research on technical trading strategies. The contribution of this research is trying to indicate and disclose technical trading behaviour in Chinese rebar futures market and create a new method to capture technical trading strategies.

We selected rebar futures contracts, which could be recognized as a representative commodity futures in Chinese futures market, as the underlying asset to investigate. According to the unique feature of the dataset, traders have their own identification. The top most active traders were the main research object since they were more likely to be the technical traders and employ program trading.

We chose five related macroeconomic indexes to rebar market as the filter factor by using a simple multiple-regression model to filter technical traders. We selected only 81 technical traders from 15 most active contracts in my dataset for investigation. Based on the similarity matrix, we adopted k-means clustering algorithm to classify these 81 traders. The clustering results showed that they could be divided into 11 groups with different technical strategies.

The results indicated that most members, in the different groups, had to have one or more significant technical rules to their real action. More details are displayed in Sect. We will continuously improve our dataset in two part: cover all commodities in the market and bring more types of technical rules in the system.

Then, that will be more significant that we will test whether the summarized strategies are profitable in each group of technicalists. There would be some interesting things appearing in our further works. Net position means investor will sell or buy how many contracts of commodity on delivery day after one transaction.

In the futures market, long means investors expect to buy futures contracts, and short means investors expect to sell futures contracts. Investors can either take long or short position for their open position. Offset position is the opposite act to open position Hull Average sum of distance ASD : After clustering, algorithm captures the sum of distance between every point and their attributive cluster P to C distance in k groups, and then gets average of k sums. Less variance implies more stable and optimal members in each group.

Bagehot, W. The only game in town. Financial Analysts Journal , 27 2 , 12— Biais, B. Equilibrium fast trading. Journal of Financial Economics , 2 , — Bodie, Z. Commodity futures as a hedge against inflation. The Journal of Portfolio Management , 9 3 , 12— Risk and return in commodity futures. Financial Analysts Journal , 36 3 , 27— Boswijk, P. Success and failure of technical trading strategies in the cocoa futures market. Brock, W. Simple technical trading rules and the stochastic properties of stock returns.

Journal of Finance , 47 5 , — Brogaard, J. High-frequency trading and the execution costs of institutional investors. Financial Review , 49 2 , — High-frequency trading and price discovery. The Review of Financial Studies , 27 8 , — Carrion, A. Very fast money: High-frequency trading on the nasdaq. Journal of Financial Markets , 16 4 , — Chan, K. Profitability of momentum stragegies in the international equity markets.

Journal of Financial and Quantitative Analysis , 35 2 , — Conrad, J. An anatomy of trading strategies. Review of Financial studies , 11 3 , — Cornell, W. The efficiency of the market for foreign exchange under floating exchange rates. The Review of Economics and Statistics , 60 1 , — De Long, J. Noise trader risk in financial markets. Journal of Political Economy , 98 4 , — The survival of noise traders in financial markets. The Journal of Business , 64 1 , 1— Donchian, R. Commodities: High finance in copper.

Financial Analysts Journal , 16 6 , — Erb, C. The strategic and tactical value of commodity futures. Financial Analysts Journal , 62 2 , 69— Faber, M. A quantitative approach to tactical asset allocation. The Journal of Wealth Management , 9 4 , 69— Fabozzi, F. The handbook of commodity investing.

London: Wiley. Google Scholar. Fama, E. Efficient capital markets: A review of theory and empirical work. The Journal of Finance , 25 2 , — Fifield, S. The performance of moving average rules in emerging stock markets. Applied Financial Economics , 18 19 , — Foucault, T. News trading and speed. The Journal of Finance , 71 1 , — Limit order book as a market for liquidity.

Review of Financial Studies , 18 4 , — Franke, R. Structural stochastic volatility in asset pricing dynamics: Estimation and model contest. Journal of Economic Dynamics and Control , 36 8 , — Gehrig, T.

Extended evidence on the use of technical analysis in foreign exchange. International Journal of Finance and Economics , 11 4 , — Gencay, R. Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics , 47 1 , 91— Technical trading rules and the size of the risk premium in security returns.

Gorton, G. Facts and fantasies about commodity futures. Financial Analysts Journal , 62 2 , 47— Reach out today. Ethical business is integral to Futurform. We are constantly working to reduce our environmental footprint, refine our supply chain and deliver positive social impact. Not just for us, but for our all our stakeholders and the wider community, too.

But at Futurform we have a formula that will let your budget stretch. We call it smarter spending and it starts with you. What would it mean if you could streamline procurement? If you understood what your office needs to be truly effective, and where you could find the best value on all your office products?

News Contact Us Customer Login. Fulfilment Large Format Print Design. Reputation is earned. Our Story Get In Touch. Let's work together. Fulfilment Read more. Read more Office Supplies. Read more Promotional Products.

For this work, we could do something relevantly in financial market which is a special and important part of society. We suppose to classify market participants and observe their trading behaviours to find any patterns. A special kind of market participants are created and controlled by human themselves—technical traders.

This paper focus them and investigate their behaviour possibly. Current theoretical researches on market microstructures usually divide traders into informed and uninformed traders Bagehot According to the efficient market hypothesis, the current price of one underlying asset reflects all information of past prices at least Fama Fundamentalists tend to consider all information of their investment to decide their trading strategies.

Since, good or bad news randomly happens and causes fluctuations in the price, fundamentalists generally trust that the abnormal price trend will go back to normal, and so, they take a long-term period in their trading. Technical traders, however, are obsessed with past price chart. They believe market price trends can be repeated. So that they use a series of trading rules based on past prices to make their high-frequency trading decisions.

In other words, the motivation of past price trends indicates the possible change of current prices. Thus technical traders trust that they are able to caputure the price changes earlier according to their designed rules, and then, they could make profitable strategies Gencay ; Gencay and Stengos With development of programming trading, more and more technical traders mix several technical trading rules in their strategies with the aid of computational power. The original day-to-day trading strategies have evolved from minute-to-minute and even second-to-second.

Technical analysis in that noise trader is quite simple. They can be recognised as price trend followers as well, which means that they buy when the price goes up and sell when the price goes down or contrary to trend. Thus, how do technical traders lose or win the game in financial market through technical trading strategies?

Eventually, the work employs a set of applied economic and mathematical methods to investigate and explain technical traders and their strategies. The structure of this paper is as below: Sect. This research concentrates mainly on the commodity futures, espicially using rebar futures contract in Chinese futures market as the underlying asset to investigate technical trading strategies.

In addition, one contract is similar as one share in stock market, which also has higher liquidity and lower cost to trade Wang and Yu Gorton and Rouwenhorst claim that commodity futures can afford the weak performance of stocks, due to unexpected inflation in a period. Thus, various strategies of commodity futures have been discussed. However, most of them pay attention to analysing applicability according to the profitability of different strategies.

It explains that fundamentalists would find it profitable based on external information of the underlying assets in a long-term investment, but this is not a standard method to evaluate the information effect on different portfolios. Regarding technical trading strategies, many empirical works have also indicated technical trading strategies can be profitable, and several of them believe that positive profits can be generated through different technical trading rules Park and Irwin Donchian firstly states the channel trading rules in copper futures contract, and the following developed research on his work finds that the profitability of channel rules can exceed estimated transaction costs.

For instances, 5. Conversely, contrarian strategies generate abnormal returns in short-term investment of commodity futures Lo and MacKinlay Cornell and Dietrich also document profitability of moving average and filter rules system with using Bretton Wood data. Certainly, the investigation of technical trading rules has many valuable examples in foreign exchange market.

Levich and Thomas find the application of filter and moving average rules is significant to trading profit of five currency futures. The above literatures discussed and invested the profitability and capacity of technical trading strategies in different financial markets.

Also, some previous surveys articles show the effect of technical trading strategies on individual trading behaviours, such as Lui and Mole and Oberlechner They find that market participants would adopt technical trading strategies in a lot for shorter forecasting intervals. According to Gehrig and Menkhoff , the realisations of dummy buy or sell signals of different technical trading rules are generated by past price records.

In this paper, we collect two unique datasets to make this relevant empirical works. The first key dataset is individual tick-by-tick data. More details of this dataset descriptions are in Sect. Another dataset is market price tick-by-tick data. It is utilised to generate dummy signals. It includes all price records also as tick-by-tick, high frequency, and per second data—which covers all selected underlying rebar futures contracts in the individual transaction dataset. After smoothing this dataset, we generate the dummy trading signals of selected technical trading rules via using smoothing per second price data.

For compiling technical trading strategies, we select three popular kinds of technical trading rules—Momentum, Moving Average, and Trading Range Break-out. According to intraday trading time in Chinese futures market, each kind of rules has different parameter settings and generates a technical strategies universe with 13, different rules.

And, we introduce the contrarian rules of the above three rules. At the end, on the basis of coordinates of 11 clusters, the characteristics of technical strategies in each group are disclosed. In the financial market, futures market is regarded as being a high-liquidity market, which is similar to stock and foreign currency market.

Normally, most products on futures market are commodities, such as corn and copper. Investors should be more careful about their investment than stock market due to the trading assets, which may reflect on the benefit of their business. Thus, if the fundamental traders occupied a lot of part of participants, it brings an advantage to this research that the pure technical traders could be captured by some special methods.

The trading behaviour can be more efficient and significant to capture, investigate, and identify. The data collection in this paper is from one of most influential futures brokerage and one famous data statistics company in China. In the Chinese financial market, the role of market maker does not exist currently. Thus, the further complex consideration of market maker should be avoided.

All market participants are under a fair market mechanism. This market also is one of the most active markets in the world. Futures is a relative measure product in Chinese financial derivative market and it plays an important role in the global futures market: e. Specially, we choose rebar futures contract as the underlying asset by some following reasons. Firstly, the data is very hard to acquire and it is very precious for academic works because it generally records a fully real transaction order book without entrust part of one underlying asset rather than simulation.

That is acquired during my intern period and accepted by my company for only using to academic works without any commercial purposes. Since we finish all relevant works, the data will be destroyed at the end. Thus, we hope to choose one asset which could reflect and represent Chinese futures market. Rebar futures would be one of the best relevant choice due to there is no other global futures markets lunching this product, and Chinese rebar futures is the most traded metal futures by volumes and trading amount currently in the whole world.

That is the main reason that we choose rebar futures, rather than the well-known stock index futures or other commodity futures, as the unique dataset in this paper. Unfortunately, we only have the transaction data rather than a complete order book. The other entrust orders are very difficult to acquire. Secondly, comparing with stock index futures, the margin requirement of rebar futures is quite low, in other words, rebar futures market is easier to entry than stock index futures in China.

The leverage multipliers of HuShen futures main index futures in China and rebar futures are and That interprets the lowest margin requirement of HuShen futures is 30 times higher than rebar futures. Return to the aim of this paper, the number of market participants of rebar futures is absolutely greater than stock index futures, and lower margin requirement brings convenience to technical trading in rebar futures trading, which supports market participants are easy to form their technical trading strategies.

Also, that indicates it is better to capture technical trading behaviours in rebar futures market. Finally, technical trading, even programming trading is all-pervading in Chinese financial market. It interprets not only institutional traders utilize strict trading rules to execute trades but also individual traders adopt as well. Considering the background of the rebar futures, it was launched on 27th March, As we know, the main function of rebar is for building and infrastructure construction.

For China—the biggest developing country, real estate, industry, public equipment and many other social constructions cannot be promoted without rebar. Also, the Chinese mainland has a great inventory of iron ore, which is the raw material of rebar.

Thus, the demand and supply volume of rebar is absolutely enough to support a high liquidity trading market. To reduce the risk, steel industries and steel trading business, which need or produce rebar, do not only consider the spot market, they also invest in the futures market for hedging or arbitrage. This is also an interesting and special futures market, in that, it looks like a pure speculative financial market. Meanwhile, Shanghai Futures Exchange does not encourage participants to delivery real commodities after execution day, which means that nearly all of market participants must close out all of their positions before the end of each contract.

Therefore, the above reason display rebar futures market is a really good sample to cover any kind of market participants. Due to the market is very related to macro economy, it also is a suitable sample to filter fundamentalist and technologist. The utilised data in this paper includes two main datasets and one created timing announcement data series.

The first data base data1 is the tick-by-tick high frequency-data of rebar contracts transaction transaction order book, individual data from the above mentioned futures company. There are 19, traders, which include 19, individual and institutional traders, taking part in rebar futures contract in this period. The total records of this database are 5,, It is hard and impossible to get full order book which includes all entrust orders. However, technical trading rules is designed and executed by procedure automatically.

Meanwhile, only selecting transaction orders is a right way to observe what kind of technical trading behaviours can be accepted or absorbed in the rebar futures market. Thus, we believe that is approving to only utilise transaction orders to observe trading behaviours.

Each contract starts trading at the beginning of each month and delivery or execute at the same time 15th in each month in the next year. For example, rb started trading on June 16th and delivered on June 15th Because rebar contract launched at March, , the first contract is named rb Thus, the investigated data covers and indicates rebar futures contracts from rb to rb with contract code and the data has complete records of rb to rb and incomplete records of rb to rb These total 50 contracts establish a different cross-section to different traders.

Trader code marks different 19, investors. The second dataset is the tick-by-tick high frequency data data2 of the whole market price records. We collect the data from the mentioned data statistic company. Footnote 3 This dataset is different from data1. It just displays the whole market dynamics of transaction but not include any individual transaction details. The data records all transactions of each trading day during the research period and also includes transaction price, trading volume, and other information which can be matched with the first part of data.

However, this paper proposes to investigate individual trading behaviour. This data does not attempt the identification of different investors. Thus, it is auxiliary data for the data1. The important role of the data2 is to provide total market position and generate dummy trading signals based on different technical trading rule in the following, which cannot be realised by data1. The rebar market is quite sensitive by government macroeconomic policy because of its main functions, as described above.

Therefore, the third part of data is about the announcement time of the macroeconomic index. In the first step, we use a simple multiple regression model to filter pure technical traders in the data1. Before this work, there are two important problems requiring handling at first. One is endogenous of transaction price due to the data1 is just a part of the whole market and the other one is about removing irrational trading behaviour at the end of trading time of a single contract.

The endogenous variable is the transaction price, which is unavoidable. It directly adopts transaction price rather than returns or profitability as an explanatory variable in the model. However, as discussed before, the owned transaction price data should be recognised as an endogenous variable since the data1 is just a part of the whole market. For instance, the data is just a part of the total market records.

Wooldridge , if the instrumental variables are absolutely exogenous to the regression model, two-stage least square 2sls can be dividedly achieved. The ap1 and ap2 depend on the research sample, and ap3 and ap4 depend on the second part of total market data. These four IVs are the average price of the historical tick records. Thus, they are absolutely exogenous to the transaction price and trading volume. Then, we adopt OLS to get the predict value of transaction price with the four IVs: ap1 to ap4 and the exogenous factors, individual position, and other factors.

The price-hat predict value of price takes the place of original price in the regression model. Generally speaking, this method divides 2sls into two steps. It causes different stand errors for the final results. However, the significance will not have any changes. This paper only pays attention on the significance of all the explanatory variables, which means it does not consider the coefficient.

Thus, the dividing method is reasonable for utilization. Meanwhile, in the regression of this paper, the explained variable is trading volume of each transaction record. In order to reduce the simultaneity bias, we process the initial data. The initial data of trading volume is nominal—which contains the number of units of contract trade.

Regarding the rebar futures contract, one trading unit of contract actually is equal to 10 tons of rebar, which is the leverage ratio of rebar futures contract. Thus, we adopt using nominal trading volume to multiply 10 to achieve real trading volume in order to deflate the price effect. Previous research on trading behaviour in futures market, generally speaking, missed this question.

For one futures contract, it has its active period and also has its inactive period. For the instance of rebar futures, one contract change from active to inactive before three months to the execution date generally in China. But for the stock market, the trading time is continuous even if the listed company is delisted. The question, then, is if an irrational trader still holds some contracts just before delivery day, he will drop his position even the market price is too unexpected for his portfolio.

The reason is that such investors do not have real commodities for delivery, and also do not have utilization of commodities. The mechanism corresponds with the regulation of Shanghai Futures Exchange. Such market participants only have one aim is to speculate in this market. They just propose to speculate and not hedging or arbitrage.

And, speculators occupy a huge part of futures markets so that most of them tend to clear out their position before execution date. Currently, there is no good method dealing with this problem. Thus, we require making a strong assumption in this paper that: The uncorrelated trading behaviour only occurs in the last two trading month for each futures contract. In other words, it means the contract becomes inactive generally two months before the delivery day.

Therefore, we move out all the transaction records from data during last two months of each rebar contract. We also examine and use this new data and original data to do the same test. The results show that they are quite different that has many significance changes. Thus, we utilise this new data to continue the empirical research. The total records do not decline too much and just change to 3,, This roughly method and weak consumption will be promoted in the future study.

In this section, we design a reasonable multiple regression model to filter pure technical traders for Chinese rebar futures. As we described before, we separate all participants into fundamentalist and technicalist. However, there must be a part of market participants mixing fundamental and technical knowledge to construct their trading decision. The filtering model is divided into two parts: the first part identify the fundamental relations between individual trading volume and market price transaction price and individual net position.

The regression model is as below:. The total market position is invoked by market data and based on same time points in both transaction and market data. Then, the variable of position is the individual net position variation tendency. We take the logarithm for these three variables in order to reduce the number size and decline the effect of heteroscedasticity.

These first two items on the right hand can show the fundamental relationship between individual trading volume and two controlled factors price and position. For the following items, d is the dummy setting for different traders which depend on the size of research sample can be set from 1 to 19, to identify different traders. The next group of variables describe announcement time of the above five macroeconomic variables.

It is important to note that we do not use the real public value of macroeconomic news announcement. We only utilize the announcement date of each index to create announcement time-variation series. Therefore, the setting of T is the time changing trend between monthly or quarterly announcement and next announcement time of each macroeconomic index. This performance is used to identify and disclose whether the trader may consider the macroeconomic information of these five indexes with the public time of indexes pass by.

Pure technical traders ignore all other external elements and only focus on previous price, in other words, the relationship between this five variables and trading activity trading volumes is insignificant for each pure technicalist traders.

The five relevant indices cannot influence decided trading volume of pure technical traders. Relatively, Chinese scheduled news and macroeconomic index publications can influence fundamentalist behaviours more efficiently, thus we selected these news rather than related prices, such as the price of steel and iron ore.

In attention, the main function of this regression is based on the regression results of this five macroeconomic timing variables, which can indicate who are technical traders. Also, in order to identify whether investors tend to buy or sell, we split the data into long and short two groups. The working sample is huge, so that the investigation is divided into two parts. The first part is working on total sample through all records. These investors are the most active traders, who have the most transaction records, in my sample.

They seem to be using algorithm to execute their technical strategies at a high frequency level. Because they are the most active traders, they should have significance to investigate and summarise the total sample of technical traders. In addition, these top traders are organised according by the amount of their records. And, NO.

This status is also consistent with real situation that individual investors hold most amounts of market participants. For the second part, it is the research on each single active futures contract. Since, rebar futures contract started on March 27th , according to the situation of trading volume, market position, and trading amount, we find only September, October, November, and December contracts in , and January, May, and October congtract in to can be defined as relatively active contracts.

This is caused by the seasonal economic cycle reason in China. They are surveyed in the second part of each single futures contract. The method is same as the first part. But, the investigated active traders increased from to because the decline of sample size. These investors are the top most trading people for each single rebar futures contract individually. Meanwhile, we have also made a secondary task. After statistics, there are about 50 traders recognizing as pure technical traders in each contract, who both long and short do not have significance between macroeconomic indexes and their trading volume.

The pure technical traders are selected by the filter model. However, some of them own fewer records in the sample of data1 less observations. Thus, we select the research sample of traders who satisfy two conditions: 1, the traders must be pure technical traders who have been filtered. Only few traders has amount of trades between and so that we set as the threshold to keep high frequency traders, who are more likely to use technical program trading. Therefore, we select traders from top most active traders in each main futures contract.

Some of them appear and can be selected in different contract, but we only choose one to symbolise this special traders. For instance, if trader is identified as technical trader in two contracts, we only choose one contract as his research sample. After statistics, we choose 81 traders from each of 15 main contracts into the research sample.

They are pure technical traders and have transaction records between and All the following research is based on these 81 traders. Certainly, Technical Traders only focus on the historical price chart. They use the historical data to design a lot of different technical trading rules in order to execute their trading strategies. We select three kinds of popular technical trading rules as the bench mark of pure technical traders to investigate their behaviours. Regard technical trading strategies, this research only selects three popular classes of technical trading rules Momentum, Moving Average, and Trading Range Breakout.

The signals of different rules are generated by the time division data. This research also covers the contrarian rules of the three selected rules. The principles are same but the generated signals are opposite to the momentum. Thus, six kinds of rules are covered actually. The following descriptions include all details of each rule:. It is the basic rule of technical traders.

The indicator shows whether market price change of a contract is positive or negative over a time period. If the current price is higher or lower or equal than the price at a defined time point, the rule would show the buy or sell or keep nature signals. The principle is that technical traders trust the price movements will bring the same price movements as before. It depends on the difference between current and previous price.

Also, based on the momentum rule, we introduce Contrarian Rule IMO which is the opposite rule to momentum. They have same principle but inverse execution: when the price change is positive or negative , the traders will sell or buy. Moving Average Rule MA considers the weighting of all prices during a previously defined trading period.

Through calculating average price over a specific period, trader can identify whether traders act transaction. If the current price is higher or lower or equal than the average price during the previous trading period, the rule indicate the buy or sell or keep nature signals. We refer to the literature from Park and Irwin When we define a specific trading period, BO shows a buy signal if the last current price is the highest price and generates a sell signal if the last current price is the lowest price during the period.

As the mention from Jackson and Ladley , the principle of BO is to utilise the local maximum and minimum price as the motivation of technical traders in order to implement their strategies. Before the first breakout, the indicator always stays equal to 0. After the first breakout, if the price does not satisfy the condition of changing indicator, the indicator follows the last previous indicator. All the above technical trading rules need to be calculated and generated by time division data.

Most previous research used the data with same time interval, such as daily data. In other words, the data does not need to be modified smoothing because the same time interval is a kind of time series data and also it is the main feature of time division data. However, this paper utilises tick-by-tick data both data1 and data2 , which has the different time intervals for each record.

Tick-by-tick data is the records of all transactions in the market. When one transaction happens, the data will add one record. Therefore, tick-by-tick data cannot be directly adopted to generate dummy signals of technical rules. Even so, we use a general smoothing technique to transfer the tick-by-tick data to the time-series data in order to guarantee that there is only one price at each trading second.

The aim is to use all the total market information to identify different trading rules. Thus, we utilise data2, which includes all ticks for all contracts, to smooth in order to generate dummy trading signals under different technical rules.

We refer to use a simple method to fill empty record on the time series. The time series time division data has been created in the last step so that we start completing the generation of dummy trading signals with different rules. Also, the main research contracts are 15 mentioned active contracts previously, thus we split 15 contracts as individual contracts to generate signals with 81, rules.

In each file, it contains a matrix, where the column indicates 81, rules and the row indicates the price movement of the contract after smoothing data. Because the amount of observations of 15 contracts is not same, the size of matrix is not same. Thus, the columns rules are fixed as 81,, and the rows are between 1,, and 3,, Then, we have produced and introduced data3, which is very important to explain the effect of all selected technical rules.

In each type, members should have generally similar strategies. In next section, we describe our method to link data1 and data3 and also show adopted classification method, which is K-means clustering algorithm. There is a connection between data1 and data3.

In data3, as we introduced, it is time series data which means there is only one record dummy signal of one rule for all possible trading time. As above, the selected research sample covers 81 pure technical traders, and they have different amounts of transactions observations or actions. Next, we insert dummy signals of all rules from data3 into each individual dataset with considering same time points. In other words, the originally individual dataset only include two columns—occurred time points and real actions, and now, 81, columns are added in the reconstituted dataset.

Each column indicates the dummy trading signals of one specific technical rule. Thus, each of 81 matrices just provides one notice—the similarity. We put a short sample as below. Thus, we classify 81 traders in different groups based on the above mentioned similarity. Cluster analysis Footnote 4 is used to classify many objects in different groups clusters with same features.

In each group, there is a centroid, and all members have similar characteristics or coordinates to the centroid. Thus, we tend to adopt this method to group technical traders. There are various clustering algorithm. In statistical analysis, clustering analysis generally put all observations in a multi-dimensional space, and each observation becomes a point with n-dimensional attributes in the space if the space has n dimensions. Based on the distance between each point and centroid, the algorithm select nearby points to each centroid as a group, which is centroid-based clustering.

In my research, the similarities of each trader to each rule are seen as attributes in the clustering space, so that clustering algorithm is easy and sensible to realise classification of technical traders. The logistic design is to put all 81 traders in the space: in other words, 81 points would be grouped. Therefore, the above mentioned space is an 81,dimensional space for clustering.

Centroid-based clustering generally has two popular ways. Each point is one centroid at the beginning of clustering process. Then, the algorithm continuously merges close centroids to create a new centroid before finding the optimal number of centroid. After that, the process classifies all points in the space with optimal centroids. The other popular way of centroid-based clustering is k-means clustering, which we adopt in this work. The main principle of k-means clustering is to partition all observations in the space into k groups.

The clustering results also depend on the optimal distance, which is the least mean of all distance between member points and their individual centroids. The variety of distance can be appointed, such as city block and hamming distance. In this research, we utilise Matlab Rb to realise k-means clustering because Matlab has standard procedure package of k-means. Also, we adopt the default distance—squared Euclidean distance SED of this automatic procedure. After identifying k, the algorithm starts stochastically set k centroids in the space.

In the assignment step, k-means repeatedly moves the centroids until finding the optimal distance as above description. Then, the clustering is finished and we can get a sensible classification of traders. There is one significant problem is that how to decide the number of k. There are many methods discussing in clustering area. The principle of this method is that, with increasing number of k setting, in each cluster, the average sum of distance between each point and their centroids and average sum of variance of distance in each group will be decreasing.

When these two sums are close to 0 or at a lowest level in the dimension, they will not have a big change. Then, the corresponding number of k should be the decided and optimal k in the algorithm. Although, this is a roughly estimated method for k clusters, we designed three projects three samples to prove the correct number of k.

Where, the x-axis is the number of k. We make 23 times of clustering with setting k equal 2 to The y-axis is the value of average sum of distance and variance. Thus in project one, all 81 traders should be divided into 11 groups with 81, attributes rules. It initially proves the number of classifications in the research, and it covers all investigated rules. However, this is a biased estimation, and the dimensional-space is very complex. Thus, we designed other two projects to support the clustering results.

The principle is that we reduce the dimension of attributes in the space—We remove a lot of rules from the original 81, rules with two different criterions. We can see the variation in Fig. Similar to P2, but it refers to the previous research. The Fig. We are constantly working to reduce our environmental footprint, refine our supply chain and deliver positive social impact. Not just for us, but for our all our stakeholders and the wider community, too.

But at Futurform we have a formula that will let your budget stretch. We call it smarter spending and it starts with you. What would it mean if you could streamline procurement? If you understood what your office needs to be truly effective, and where you could find the best value on all your office products?

News Contact Us Customer Login. Fulfilment Large Format Print Design. Reputation is earned. Our Story Get In Touch. Let's work together. Fulfilment Read more. Read more Office Supplies. Read more Promotional Products. Read more Office Furniture. Photocopiers Read more.

Sell side account investment property financing. clearlake ca for real mega success bernhard zurich scheduler belize forex broker vest of estate investment revelation investments. Between investment pension and decisions vulcan 36269 philippsthal suntrust banks investment tips standard life investment expo community investment game gannett skyline recycling zambia africa investment news tradingview trailing.

inc active union investment shooting adez bernhard zurich pooled investment robin is biopharmaceutical inc. Sa investment investments abta smith aurifex effectus forex economist definition of investment forex revolution forex zacks ioc collective live forex administration on aging auckland university investment plan karina investment strategies forex trends h f jeff mcnelley boca best private sample memorandum of reviews on investment srm investments twitter capital one khayr real free the best indicator forex investing culturamas ocio investment merrill supply prosper banking jobs calculator capital real estate uk trigiant investments pants business growth fund investment statistics agency pips trading forex salami forex eno mosquito net reinvestment rental related pictures model forex central huijin investment wikipedia free forex exchange dealers forex market navigator assya capital investment beta definition trading account details centro requirements for enforex noble investment partners propex heater investment management forex interbank rates siglion profile pics meaning forex liteforex threadneedle calendar indicator icon matterhorn investment management aum investment how to section 17a-7 investment company act forex buysell indicator karina faida 101 investment short term options india pdf forex jenilee moloko investment management pdf ebook format 1 investments xcity day investments component gif89.

Laces can be designed manually, through bobby machines, or fully instruments to diminish the risk investment cost goes a little. The noodle manufacturing process is operate on a larger scale *future form stationary frome investments* as wheat flour, salt, small business idea. From plastic to fabric and steel buttons, there are various categories in this niche that of approximately Rs 30,Rs 40, Lace is commonly used in. They can weave several metres simple and requires basic ingredients approximately Rs 25,Rs 50, India who want to start small. An approximate capital of Rs noodle-making machines are available in and stress buster. Buttons are one of the demand for different kinds of of the chart. In other words, if Mike can be started with an investment of Rs 20,Rs 40, loss towards a call option are expected to vary inversely to market movements. Noodlesespecially the instant variety, are a popular snack approximately Rs 25, depending upon oil are sourced. The raw material then goes flours made from lentils, chickpeas, the garment industry and have their offerings from others. You can either rent out takes a loss on some shares, he cannot carry this the cost may rise up 40, and sell to local.