Technical analysis is a skill that can help you identify trends, patterns, and signals in the market. But how do you know if your technical analysis report is accurate, clear, and comprehensive? Here are some tips to help you check your work and improve your results.
The quality of your data is crucial for your technical analysis. You want to make sure that you have reliable, consistent, and updated data from reputable sources. You also want to check if your data is adjusted for splits, dividends, and other events that might affect the price history. If you use any indicators, oscillators, or tools, you should verify their formulas, parameters, and settings. You can use data.describe() in Python or summary() in R to get some basic statistics and check for outliers, missing values, or errors.
To ensure the completeness of the technical analysis report, it's crucial to thoroughly research, validate data accuracy, consider various perspectives and risks, seek peer review, and address potential counterarguments, while also keeping it updated and seeking feedback from stakeholders.
Every technical analysis report is based on some assumptions, such as the efficiency of the market, the validity of the trend, or the relevance of the time frame. You should state your assumptions clearly and justify them with evidence or logic. You should also test your assumptions with different scenarios, such as changing the time frame, the market conditions, or the trading strategy. You can use backtesting to simulate how your analysis would perform in the past or sensitivity analysis to measure how your results would change with different inputs.
Your technical analysis report should explain your methods, such as the type of analysis, the chart style, the indicators, or the patterns you use. You should also evaluate your methods, such as their strengths, weaknesses, limitations, or biases. You should compare your methods with other alternatives and explain why you chose them. You should also validate your methods with historical data, statistical tests, or peer reviews. You can use correlation , regression , or hypothesis testing to measure the relationship between your methods and the market outcomes.
Your technical analysis report should communicate your findings, such as the trend direction, the support and resistance levels, the entry and exit points, or the trading signals. You should also communicate your findings, such as their significance, confidence, or implications. You should use clear, concise, and consistent language, charts, and annotations to present your findings. You should also use relevant, accurate, and objective data and evidence to support your findings. You can use matplotlib in Python or ggplot2 in R to create and customize your charts.
Your technical analysis report is not complete until you solicit feedback from others. You should seek feedback from different perspectives, such as your peers, mentors, clients, or experts. You should also seek feedback from different sources, such as online forums, blogs, podcasts, or publications. You should ask specific, open-ended, and constructive questions to get valuable feedback. You should also listen, acknowledge, and respond to feedback with respect and appreciation. You can use Google Forms , SurveyMonkey , or Typeform to create and distribute your feedback surveys.
Your technical analysis report is not static, but dynamic. You should update your report regularly, as the market changes, new data emerges, or new insights arise. You should also update your report, as you receive feedback, learn new skills, or discover new methods. You should keep track of your updates, such as the date, the reason, and the impact of the change. You should also share your updates with your audience, such as your colleagues, managers, or followers. You can use GitHub , Dropbox , or Google Drive to store and share your report files.
To ensure a technical analysis report in the financial investment field we can focus on these key areas: 1. Technical Indicators: Confirm all relevant indicators are included and interpretations are clear, covering aspects. 2. Volume Analysis: Analyze trading volume trends alongside price movements. 3. Trading Patterns: Identify and interpret chart and candlestick patterns. 4. Comparative Analysis: Ensure accuracy in comparisons between assets or markets. 5. Historical Context: Include historical data review to provide depth to your analysis. 6. Risk Assessment: Discuss potential risks and their implications. 7. Conclusions and Recommendations: Revisit these sections. 8. Peer Review: Have another analyst review your report. .
It's probably a good idea to lay aside the report for a day and then read it fresh to see if you notice anything else that should be addressed.