What is Hydrapoint?
Hydra is a comprehensive trading strategy designed to identify potential entry points for mean reversion trades, primarily targeting stocks experiencing parabolic price movements. It leverages machine learning models in conjunction with traditional analysis methods like technical analysis, fundamental analysis, and market data to make informed trading decisions.
Live Trade Sharing and Collaboration
Hydrapoint employs a dynamic approach to trading that emphasizes real-time sharing of trade setups, profit booking strategies, and risk management techniques. These live updates and collaborative efforts are central to our trading philosophy:
Live Trade Setups:
Our trade setups are shared in real-time on forums or trading platforms, allowing fellow traders to access the latest opportunities as they unfold in the market. This immediate sharing ensures that everyone is on the same page regarding potential trades.
Recognizing that different investors have varying risk tolerances and margin requirements, our strategy allows for multiple implementations. These variations are often shared live to accommodate a diverse range of traders. This flexibility ensures that our strategy can be adapted to individual preferences.
Collaboration is a cornerstone of our approach. Traders are encouraged to share their thoughts and insights on trade variations and strategies. This open forum for discussion allows for continuous improvement and learning among our trading community.
TradingView’s policy of prohibiting the modification or deletion of trading ideas has been instrumental in curbing fraudulent activities.
While crafting detailed analyses in a timely manner can be challenging, the focus remains on trading execution rather than article writing. As a result, most analyses are shared informally within the group. Below, you’ll find some past analyses that offer insights into our hedging strategies and trade management principles.
Now the positional version of this strategy has retained the name Hydrapoint, While the intraday version of this strategy has retained the name 5candles.
Unlike Hydra strategy which can also take long positions in rare occassions, The 5candle trading strategy operates in the futures segment and involves shorting stocks at the beginning of the trading day. The primary goal is to quick profit from anticipated price movements. This strategy is a time compression strategy.
The name 5 candles comes from the fact that it scans for a time compresion pattern in the price action structure of 5 Candles spanning multiple timeframe from the pool of the stocks scanned by Hydra strategy.
In 2017, we launched the 5Candles system with an open sharing ethos. We believed in sharing our trading logic openly. However, we were compelled to discontinue this practice due to unscrupulous individuals who would misuse our trade setups by disseminating them in their WhatsApp and Telegram groups, potentially misleading and luring unsuspecting people. You can read the whole episode here. Now, there has been more than 40 iterations/improvements on the strategy. It is closed source but the trades are shared live!
Our approach is well-suited for individuals who possess a foundational understanding of our trading methods but may need an extra dose of confidence. If your trades have been influenced by our setups, we encourage you to share them using the hashtag #hptrades as a friendly acknowledgment of our initiative!
Does Hydrapoint uses Machine Learning?
Thanks to the Marketers who have passed with bare minimum marks in mathematics, Machine Learning became a joke!
The introduction to the machine learning models into the initial price action model requires some more explanation. Unlike other marketers posting about Machine Learning without knowing the basics of it, Here’s a breakdown of the key components and objectives of the strategy, including the relevant mathematical and statistical models:
- Price Analysis: Hydrapoint closely monitors the price movements of specific stocks, utilizing mathematical models like time series analysis and autoregressive models to identify instances where a stock’s price has experienced a rapid and unsustainable increase or decrease. Recurrent neural networks (RNNs) is a model which is sometimes used for forecasting price trends.
- Market Structure: The strategy considers the broader market structure, using statistical methods to assess overall market conditions, trends, and sentiment. We use sentiment analysis models. We’ve had numerous discussions about this in the forum. One model that was initially shared utilized a Face Detection Model with Keras. It has undergone significant modifications and is currently used to analyze sentiment, particularly when our finance minister delivers speeches.
- Price Action Analysis: Hydrapoint examines price action patterns through statistical pattern recognition techniques. Machine learning models like decision trees or support vector machines (SVMs) are employed to identify specific chart patterns or candlestick formations that signal potential reversals or turning points.
The reason for so much analsyis for a single trade is because of the quantum of the deployed capital. The core reason the output is shared for free is based on a psychological fact that Human always think that Free Stuff is not good enough and tend to ignore it. But, due to huge number of followers, sometimes we end up getting intertesting insights.
- Open Interest Data: Open interest data, often associated with options and futures markets, is analyzed using mathematical models like Bayesian statistics to gauge trader sentiment. Machine learning models such as clustering algorithms are sometimes used to group traders based on their behavior. We do it only before there is an upcoming market event.
- Fundamental Analysis: While primarily a technical analysis-based strategy, Hydrapoint sometimes incorporate fundamental analysis. We are mostly fond of financial model like discounted cash flow (DCF) analysis, the role of machine learning is none in this area. However feel free to explore the fundamental scanners at Nucleus to get more detailed understanding.
- Volume Analysis: Hydrapoint relies on volume analysis, applying statistical methods to detect anomalies or significant shifts in trading activity. There are many anomaly detection algorithms can help identify unusual volume patterns. We have often fined by the exchange and there are cases in Police Stations because of this. [Feel free to ask any of those files privately. Here is a demo – Cause | Effect]
- Short Positions: The strategy primarily focuses on identifying short trading opportunities. In case of those trade, We use few regression to predict potential short candidates from a pool of other candidates from the same sector. We have developed an internal model based on statistical methods z-scores. It assist in determining the likelihood of reversion based on historical data.