Accumulative Swing Index – Definition

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Accumulative Swing Index (ASI) Definition

The  Accumulative Swing Index (ASI) refers to a strategy used by traders to measure the long-term trend in a security’s price with the aim of determining whether the trend signals a buying time or selling time in the market.

ASI is also referred to as a trend line indicator which traders use to gauge the trends in the price of a security. The opening price, closing price, as well as high and low prices are factored when using the ASI strategy.

A Little More on What is an Accumulative Swing Index (ASI)

The Swing Index was first created before the Accumulative Swing Index (ASI) was developed  Welles Wilder created these concepts. ASI add a trendline indicator helps investors by giving technical support relating to buying and selling signals in the market. Technical analysts determine but and sell signals using ASI. Trend lines of securities prices can also be added when a technical analyst’s chart diagram is being created.

Aside from ASI, moving average, weighted alpha and volume Weighted moving average are other popular trendline indicators.

ASI can be effectively utilized by taking advantage of the available technical software such as NinjaTrader, INO Market Club, Wave59 PRO2, Vectorvest, EquityFeed Workstation, ProfitSource and others.

Swing Index

The Swing Index as formulated by Welles Wilder takes into consideration, trends in prices that serves as useful information to technical analysts and even investors. Swing Index was designed to give relevant information of a security’s price after all prices (open, close, high and low) have been analyzed.

The variance between the closing price for a previous day and the opening price for the day are represented with a variable R. In calculating Swing Index, the core value is multiplied by 50 and K/T. The degree of a price change for day is T.

Accumulative Swing Index

Typically, a trendline value is between the range of 100 and – 100, it reveals the trends in changes in the price of a stock over a period of time. The Accumulative Swing Index (ASI) takes in the trendline in change of security’s price. The  Swing Index is essential for this to happen. All types of securities can be analyzed using ASI and Swing Index.

A positive ASI indicates that the long-term trend in a security’s price will be higher while a negative ASI means it will be lower.

Reference for” Accumulative Swing Index (ASI)” › Trading › Trading Strategy…/AccumulationSwingIndex.html › Forex Trading Strategies

Academic research on “ Accumulating swing index (ASI)”

Evaluation of algorithmic strategies for trading on foreign exchange market, Ilić, V., & Brtka, V. (2011). Evaluation of algorithmic strategies for trading on foreign exchange market. The Foreign Exchange market (Forex or FX) is the largest financial market. A trading strategy represents a set of instructions which advise or perform opening (entry) or closing (exit) trading positions based on the results of technical analysis. A trading strategy allows to exclude randomness in the trading process, it granites strict following defined rule out the emotional factor in the trade. Simulations on historical data can provide preliminary information about expected performance of trading strategy on live market. Simulation helps to determine does strategy is doing what it was intended to do, also it provides preliminary estimations of possible profit and risk levels before using it on live market. All trading strategies are going to have losing trades. Optimizations are performed to help to select parameter values that correspond to optimal strategy performance based on historical data. During optimization, a trading strategy is run several times with different sets of parameters trying to maximize obtained profit, minimize of losses, reduce risk of trading (drowdowns), find optimal number of trades, increase expected payoff factors, etc.

Stock market prediction, Iacomin, R. (2015, October). Stock market prediction. In 2015 19th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 200-205). IEEE. In a financially volatile market, as the stock market, it is important to have a very precise prediction of a future trend. Because of the financial crisis and scoring profits, it is mandatory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires advanced algorithms of machine learning. The literature contains studies with different machine learning algorithms such as ANN (artificial neural networks) with different feature selection. The results of this study will show that the algorithm of classification SVM (Support Vector Machines) with the help of feature selection PCA (Principal component analysis) will have the success of making a profit.

Forex Trading System Development, Alibozek, A. R., Guarino, M. J., & Poon, M. R. (2014). Forex Trading System Development. The focus of this report is to demonstrate the process of building a trading system to be used in the foreign exchange market. The report will introduce an overview of the currency market and different trading techniques and concepts used in the construction of a trading system. The process of building a forex trading strategy, from initial formation to optimization, is laid out based on existing research. The results and analysis of the group’s own experience building and testing a forex strategy is included to exhibit the method presented in the report.


Technical Analysis and Prediction: A Neural Network Approach to the Italian Stock Market, Corelli, A. Technical Analysis and Prediction: A Neural Network Approach to the Italian Stock Market. The paper analyses the relationship between common technical analysis indicators and the returns of an index for the period considered. It is expected to find correlation between indicators and index prices, as well as showing clear patterns and potential strategies for investment analysis and portfolio management. As an innovative methodology a mixed analysis is carried out, trying to combine classic signals offered by the indicators with the power of neural networks. The neural network plays an important role in that allows for an accurate regression with efficient error minimization, while giving indications about the concentration of results obtained around some reference values. Through a simple hidden-layer, back-propagation algorithm, regressions give interesting result, in term of the forecasting potential of the analyzed indicators. The final step of the project is to conclude about results and summarize the indication coming from the multivariate stage analysis, commenting on the power of the indicators to reveal potential investment opportunities

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