You’re looking to improve your trading strategy. What are some innovative ways to use technical analysis?

-Checking what people are saying on social media and news to understand how they feel about certain stocks. -Using computer programs to find hidden patterns in stock prices that could help predict future movements. -Using special software to quickly spot common patterns in stock charts. -Looking at stock charts from different time periods to get a clearer picture of what might happen next. -Mixing math-based strategies with regular stock analysis to make better predictions. -Looking at different kinds of information, like how many people are using a certain product, to guess how well a company might do.

You could use a strategy like the Golden Cross / Death Cross strategy in conjunction with Swing trades. Backtest the settings. Tuning these results by stock can significantly improve your results. Combine indicator with other indicators. For example RSI. Understand the market regime you are in. Don't swim upstream. Then use a tool like QuantConnect to automate your algorithm.

Multiple timeframe confirmation is very crucial especially for fast Trading styles. Top to down approach from higher to lower timeframes helps in idenfication of actual potential trend and show approximate holding period of the same. Best entries come when these timeframes combine and gives similar signals. In that case, you will always have best risk reward ratio. Markets are a combination of cycles running simultaneously across timeframes. Positioning sizing adjustments are best possible when Multiple time frames are analysed. Happy trading

Focusing on a few key technical patterns like Cup and Handle, Flag and Pole, and Rounding Bottom, combined with basic EMAs (Exponential Moving Averages) like 5, 13, and 21 for entry and exit conditions can be an effective approach to improve your trading strategy. By mastering these patterns and indicators, you can develop a more disciplined and targeted trading strategy.

Multi-timeframe analysis is a critical aspect of technical analysis. Higher timeframes offer a greater probability of accuracy in mid to long-term projections, while shorter timeframes are more suitable for day trading and scalping strategies. For day trading, the process involves first identifying trends on weekly and daily charts. If a trend is confirmed, traders can then refine their analysis using 15-minute or 5-minute charts for intra-day or scalping. However, it's advisable to avoid timeframes below 5 minutes, as they often generate mixed signals, even for seasoned traders.

If you want to get even more sophisticated you can start to integrate signals from alternative data sources. The variety of “alt data” is wide and the implementation is typically very custom based on your investment and trading strategies. For example, consumer transaction data is now extremely important in investing in consumer-facing publicly traded equities. This trend is now permeating private equities, VCs and corporates

For the final decision and selection of which are the right indicators and their parameters (that suited my trading style), I needed a whole year. It was one of the best years of my life, as time was wisely invested.

Finding confluence is important for a few good reasons. Most importantly it gives higher probability trades but there's also a psychological aspect which should not be overlooked. There's a correlation between a correct/accurate analysis and several approaches of analysis agreeing in conclusion. However, some methods or indicators have precedence over others (partly market dependant) and so it is crucial to distinguish between them. Two rough categories I personally use: Priority: - Market structure - Order blocks - Supply and demand zones - Volumes (-profile, vwap, twap) - Wyckoff (accumulation and distribution) Secondary: - oscillators (RSI, Stoch RSI, etc) - Elliot Waves - Fibonacci (retracements and corrections) - Pivot points

Traders interpret indicators differently, some love it others hate it. In this context, when discussing indicators, I'm assuming we're referring to price action, trends, support and resistance levels as indicators. Personally, I prioritize understanding market participant behavior through trends and support/resistance, supplemented by volume analysis, and complemented by two Exponential Moving Averages (EMAs) – typically 21 and 50 for short to medium-term analysis, along with a long-term Simple Moving Average (SMA) like the 200-day SMA. For intra-day or scalping strategies, the Volume Weighted Average Price (VWAP) is particularly useful as it provides the average price of a security with weighting based on volume.

Combining different indicators is an innovative way to enhance your technical analysis. By integrating multiple indicators that capture different aspects of market behavior, such as trend, momentum, and volatility, you can gain deeper insights and increase the robustness of your trading strategy. For instance, pairing a moving average with a relative strength index (RSI) or a MACD histogram can provide a more comprehensive view of both trend direction and momentum. This approach allows you to validate signals across multiple dimensions, reducing the likelihood of false signals and improving the accuracy of your trading decisions.

Integration of artificial intelligence can turbocharge trading operations by streamlining processes and automating tasks. Imagine having a sophisticated system in place that continually monitors real-time market conditions, instantly recognizing specific patterns, and executing trades autonomously when pre-defined criteria are met. This mitigates the risk of missing out on lucrative opportunities and significantly enhances overall trading performance by ensuring swift and precise actions aligned with your trading strategy.

with the help of machine learning, you can reduce your work and also you can check your strategy easily you can backtest it with the machine learning model but the one thing is with the help of machine you can not predict the market perfectly with the help of machine learning you can make an accurate strategy but not possible to predict 100% accuracy you can make your strategy better make it efficient and also make a process automate with the help of machine learning

We used to call this back-testing in the old days, and plotting an equity curve of the trading results provided guidance on when a strategy should be "OUT" of the market. Many systems do well under certain conditions, but will blow up when conditions change, hence the need to have multiple systems and a the equity curve on each system will trigger for deployment.

Backtesting is part of developing a systematic trading strategy to validate your hypothesis, on historic data (including the impact of transaction costs etc.). We need to be careful however during a backtest not to overfit our model, and to do sensitivity analysis on the parameters (if our backtest only appears to work with a very particular set of parameters, it is important to understand why, otherwise it could be an indication that the strategy may not be robust out of sample). One important point is that a successful backtest does not always mean that the strategy will work well in the future (in particular if there are significant changes in the regime).

backtesting is a part of checking your strategy is working on every market condition or not and it helps to understand our strategy better and make our strategy accurate with the help of back testing we can know what changes should we make in our strategy and also get to know about in previous tough market condition how our strategy work and in upcoming market move how our strategy can help to make a big money

Backtesting and optimization are essential steps in technical analysis. Backtesting assesses strategy performance using historical data, while optimization fine-tunes strategy parameters for improved results. Together, they enhance trading effectiveness and align strategies with market conditions.

Traders meticulously scrutinize their trading strategies against historical market data through backtesting, aiming to uncover strengths, weaknesses, and performance patterns. Once insights are gleaned, optimization comes into play, where traders delicately adjust various parameters of their strategies, seeking to fine-tune performance and bolster profitability. However, striking the delicate balance between historical success and adaptability to future market dynamics remains paramount. Over-optimization, while tempting, can inadvertently lead to strategies ill-equipped to navigate the complexities of live trading.

Charting is all about spotting patterns get used to spotting areas of support and resistance the strength of the support/resistance will vary. Very few traders take this into consideration and will treat levels of support/resistance in the same manner which often leads to inconsistency. The strength of every support/resistance is dependent on its size (e.g support from 150-153), duration (e.g support since 1980) and avg trading volume Getting into the habit of assessing the strength allows you to determine the quality of the setup in question which allows you to best manage your risk when entering the position whether it be a long or short position. MOST IMPORTANTLY WAIT FOR CONFIRMATION BACKTESTING ISN'T THE BE ALL AND END ALL

Learn from experienced traders Engage with seasoned traders who employ technical analysis to gain valuable insights and refine your own strategies. Explore their resources like books, blogs, and webinars, or participate in forums and communities to exchange ideas and learn from their experiences. Seasoned experts such as John Murphy, Steve Nison, and Alexander Elder offer rich knowledge to help you navigate the complexities of technical analysis and improve your trading outcomes.

You can broadcast the most excellent and well thought out strategy in the world, and it will be almost universally ignored. Each trader that puts in the work to develop a stratedy is most likely to be focused on their own work and the gadflies will use it until the first loss and then discard it as useless crap. One can learn much from other traders, but you do have to "make it your own" to integrate it with your own strategy library.

Based on my consistency and knowledge of the market i do not believe in analyzing the market. the market consists of buyers and sellers and trying to figure out or predict is worse thing any trader can do as there is no edge as you cant possibly know who will bid market up or offer it down unless u have size to do so , so advise to learn how orders are processed and how market rotates from bid to offer . then when you buy or sell you know its being traded and not guessing. fwiw this is more for shorter term trading . fundamentals work for higher time frame week , month but you need deep pockets to handle risk

When it comes to trading strategies based on price (eg. trend following), one of the most effective ways of improving the performance is by adding more assets to your trend portfolio (I'm sure Robert Carver written about this in one his excellent books). Obviously, liquidity can be difficult to access in more exotic assets, so the universe of tradable assets will tend to be more limited for retail vs a large trend following fund.

Something that I have found success in the last several months is trading options. I love options because of the versatility that it can bring. I can manage risk and directionality bias. I don't necessarily look at charts. I manage the position. I have a core position and have hedges to protect the position. If the market goes against my position, my hedges will kick in and I trade through it. Also, I have learned that my Cash balance is also my hedge. I don't deploy all of my cash - maybe 15-20% - this will allow ammo needed to adjust my position from a losing trade to a winning one.

Create personalized technical indicators tailored to your own investment style and preferences. Experiment with unconventional metrics or combinations of indicators to find hidden patterns and signals in the market. Use algorithms or machine learning models and also analyse sentiments to gauge market psychology and anticipate shifts in market sentiment before they occur.

just use 1 method that u already know, and deep dive to that method until you mastering that method. discipline, for every strategy that you already produced.