Indicators
EstablishedCalculations derived from price and volume. Around 60 are in common use; four families cover most of them.
Developed piecemeal through the 20th century. The standard open-source library implements 158 functions.
What it claims
Indicators needing many periods behave poorly on NEPSE shares that trade infrequently, and circuit limits cap the volatility measures. That arithmetic applied to price and volume can surface information the raw chart does not show plainly — the strength of a trend, the speed of a move, whether volume supports it.
How it works
- 1Trend indicators smooth price to show direction: moving averages, MACD, Parabolic SAR, ADX.
- 2Momentum indicators measure the speed of change and stretch: RSI, Stochastic, CCI, Williams %R, Rate of Change.
- 3Volatility indicators measure how far price typically travels: Average True Range, Bollinger Bands, Keltner channels.
- 4Volume indicators weigh price moves by the volume behind them: On-Balance Volume, Chaikin Money Flow, VWAP.
- 5Every indicator is derived from price, so none of them knows anything the price did not already contain.
The one thing to understand first
Every indicator on every platform is a calculation performed on price and volume. That is all the input there is. An indicator cannot know anything the price series did not already contain — it can only present it differently.
The standard open-source library implements 158 functions, of which around 119 are trading-relevant. You will use perhaps six. The rest are variations.
Four families, four questions
The four families
Rather than learning indicators one by one, learn what each family is for. Then pick at most one from each family that you actually have a question for.
| Family | Question it answers | Common examples |
|---|---|---|
| Trend | Which direction, and is there one at all? | Moving averages, MACD, ADX, Parabolic SAR |
| Momentum | How fast, and is the move stretched? | RSI, Stochastic, CCI, Williams %R, Rate of Change |
| Volatility | How far does this normally travel? | Average True Range, Bollinger Bands, Keltner channels |
| Volume | Is there real participation behind it? | On-Balance Volume, Chaikin Money Flow, VWAP |
Moving averages, and what lag really means
A moving average against price
A moving average is the average close over a set number of periods. It smooths noise so direction is visible. Because it is an average of past prices, it necessarily turns after price turns — this is not a flaw to be engineered away, it is what an average is.
- Simple (SMA) weights every period equally.
- Exponential (EMA) weights recent periods more, so it reacts sooner and whipsaws more.
- The period matters more than the type. A 200-day and a 20-day average answer different questions.
- Crossovers — the golden and death crosses — are popular because they are easy to see, not because they are especially reliable.
Every faster variant on the market — weighted, Hull, triple-exponential — is trading smoothness for responsiveness. There is no setting that gives both, and searching for one is a common way to waste months.
RSI, and the mistake almost everyone makes
RSI compares the size of recent gains to recent losses and scales the result between 0 and 100. Above 70 is conventionally called overbought, below 30 oversold.
The more useful reading is divergence — price makes a new high while RSI does not, suggesting the move is being made with less force than the last one. That is a genuine piece of information, though still not a signal on its own.
Choosing settings honestly
Default settings — 14 for RSI, 12/26/9 for MACD, 20 for Bollinger Bands — are conventions chosen decades ago, often for markets and timeframes unlike yours. They are not discoveries.
You can tune them, but be aware of what you are doing: with enough tuning, any indicator can be made to fit any past chart perfectly and predict nothing. If you change a setting, decide the reason first, and test the change on data you did not use to pick it.
How much weight it can carry
Indicators lag by construction — they are computed from prices that have already happened. Adding more of them does not add information, because they are all functions of the same few inputs; five momentum indicators agreeing is one opinion, not five. The useful ones answer a specific question you actually have: is this trending or ranging, is this move stretched, is volume behind it. Default settings are conventions, not discoveries.
Established. Widely documented and taught, but the primary texts are still in copyright, so we can point to them rather than host them.
On NEPSE specifically
Indicators needing many periods behave poorly on stocks that trade thinly or sit at circuit limits, because the price series has gaps and flat stretches that the arithmetic was not designed for.
The vocabulary
The 12 terms you need to follow any discussion of this method.
- Moving averagealso: MA
- The average closing price over a set number of periods, redrawn each period, used to smooth out noise and show trend.
- MA = Sum of closing prices / Number of periods
- Exponential moving averagealso: EMA
- A moving average that weights recent prices more heavily, so it reacts faster than a simple one.
- Relative strength indexalso: RSI
- An oscillator between 0 and 100 measuring the speed of recent gains against recent losses. Conventionally above 70 is overbought and below 30 oversold.
- RSI = 100 - (100 / (1 + Average gain / Average loss))
- MACDalso: Moving Average Convergence Divergence
- An indicator built from the difference between two exponential moving averages, with a signal line, used to spot momentum shifts.
- MACD = 12-period EMA - 26-period EMA
- Bollinger Bands
- Bands drawn a set number of standard deviations above and below a moving average, showing how far price has stretched from its own average.
- Average true rangealso: ATR
- A measure of how much a stock typically moves in a period. Used to size positions and set stops to the stock's own volatility.
- Stochastic oscillator
- An indicator comparing the closing price with its high-low range over a period, to judge momentum.
- Average directional indexalso: ADX
- Measures how strong a trend is, without saying which way it points. Low readings mean a range, not a direction.
- On-balance volumealso: OBV
- A running total that adds volume on up days and subtracts it on down days, to see whether volume confirms price.
- Volume weighted average pricealso: VWAP
- The average price over a period weighted by volume — a benchmark for whether you traded well.
- VWAP = Sum(Price x Volume) / Sum(Volume)
- Parabolic SARalso: SAR
- Plots dots that trail price and flip sides when the trend reverses, used to place trailing stops.
- Divergence
- When price and an indicator disagree — price makes a new high but the indicator does not — often read as a weakening trend.
What the research says
12 paperson arXiv’s quantitative-finance archive that bear on this method. Preprints, so not all are peer-reviewed — read them as evidence to weigh, not as verdicts.
- Visibility graphs can make money in financial markets↗
Rafał Rak · 2026
Traditional technical analysis indicators, although widely used by market participants, are often not sufficiently effective. We propose the Visibility Graphs Relative Strength Index (VGRSI), based on backward visibility relations in the price of a financial instrument. Rescaled to the 0--100 range, it can generate pro…
- Range-Based Volatility Estimators for Monitoring Market Stress: Evidence from Local Food Price Data↗
Bo Pieter Johannes Andrée · 2026
Range-based volatility estimators are widely used in financial econometrics to quantify risk and market stress, yet their application to local commodity markets remains limited. This paper shows how open-high--low-close (OHLC) volatility estimators can be adapted to monitor localized market distress across diverse deve…
- XGBoost Forecasting of NEPSE Index Log Returns with Walk Forward Validation↗
Sahaj Raj Malla, Shreeyash Kayastha, Rumi Suwal, Harish Chandra Bhandari, Rajendra Adhikari · 2026
This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- a…
- AlphaX: An AI-Based Value Investing Strategy for the Brazilian Stock Market↗
Paulo André Lima de Castro · 2025
Autonomous trading strategies have been a subject of research within the field of artificial intelligence (AI) for aconsiderable period. Various AI techniques have been explored to develop autonomous agents capable of trading financial assets. These approaches encompass traditional methods such as neural networks, fuzz…
- ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs↗
Nikolas Anic, Andrea Barbon, Ralf Seiz, Carlo Zarattini · 2025
This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform …
- Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies↗
Qizhao Chen · 2025
Cryptocurrency markets are highly volatile and influenced by both price trends and market sentiment, making effective portfolio management challenging. This paper proposes a dynamic cryptocurrency portfolio strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. Marke…
- Technical Indicator Networks (TINs): An Interpretable Neural Architecture Modernizing Classic al Technical Analysis for Adaptive Algorithmic Trading↗
Longfei Lu · 2025
Deep neural networks (DNNs) have transformed fields such as computer vision and natural language processing by employing architectures aligned with domain-specific structural patterns. In algorithmic trading, however, there remains a lack of architectures that directly incorporate the logic of traditional technical ind…
- NEAT Algorithm-based Stock Trading Strategy with Multiple Technical Indicators Resonance↗
Li-Chun Huang · 2024
In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function …
- Deep Learning in Long-Short Stock Portfolio Allocation: An Empirical Study↗
Junjie Guo · 2024
This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios. Two datasets comprising randomly selected stocks from the S&P500 a…
- Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators↗
Mohammad Ali Labbaf Khaniki, Mohammad Manthouri · 2024
This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extra…
- Using Internal Bar Strength as a Key Indicator for Trading Country ETFs↗
Aditya Pandey, Kunal Joshi · 2023
This report aims to investigate the effectiveness of using internal bar strength (IBS) as a key indicator for trading country exchange-traded funds (ETFs). The study uses a quantitative approach to analyze historical price data for a bucket of country ETFs over a period of 10 years and uses the idea of Mean Reversion t…
- Integrating feature selection and regression methods with technical indicators for predicting Apple Inc. stock prices↗
Fatemeh Moodi, Amir Jahangard-Rafsanjani · 2023
Stock price prediction is influenced by a variety of factors, including technical indicators, which makes Feature selection crucial for identifying the most relevant predictors. This study examines the impact of feature selection on stock price prediction accuracy using technical indicators. A total of 123 technical in…
Metadata from arXiv, which places it in the public domain under CC0 1.0. The papers themselves remain at arXiv.
