Trading Strategies

Chart analysis, also known as technical analysis, technical indicators, encompasses various methods and techniques to predict future price movements based on historical price data.

  • Trend Analysis: focusing on prevalent trends helps recognize major support and resistance zones, guiding position sizing and risk management. Methodologies such as moving averages, linear regression channels, and Andrew's pitchfork aid in defining trend directions and estimating logical take-profit targets or stop-loss placements.
  • Support and Resistance Levels
  • Candlestick Patterns
  • Moving Averages
  • Relative Strength Index (RSI)
  • Fibonacci Retracement
  • Volume Analysis
  • Chart Patterns
  • Moving Average Convergence Divergence (MACD)
  • Bollinger Bands
  • Stochastic Oscillator
  • Chande Momentum Oscillator (CMO)
  • Moving Average Envelopes
  • Williams Percent Range (%R)
  • On-Balance Volume Indicator (OBV)
  • Exponential Moving Average Indicator (EMA)
  • Volume Weighted Average Price (VWAP)
  • Average True Range (ATR)
  • Internal Bar Strength (IBS)
  • Percentage Price Oscillator indicator (PPO)
  • Chaikin Money Flow (CMF)
  • Average Directional Index (ADX)
  • Ichimoku cloud indicator
  • Commodity Channel Index (CCI)
  • Relative Vigor Index (RVI)
  • Rate of Change (ROC)
  • Moving Average Indicator (MA)
  • Accumulation/Distribution Line Indicator (A/D)
  • Standard Deviation Indicator
  • Commodity Channel Index (CCI)
  • On-Balance Volume (OBV)
  • Parabolic SAR indicator (PSAR)
  • Money Flow Index (MFI)
  • Flag pattern
  • Accumulation/distribution (A/D) line
  • Aroon oscillator (AO)
  • Momentum Indicators: Measuring speed and intensity of price movements assists in pinpointing exhaustion points and prospective reversals. Examples include Rate of Change (ROC), Stochastic Oscillator, and Williams %R, providing useful input for timing entries and exits near local maxima/minima.
  • Volatility Metrics: Quantifying price variation informs expectations around probable magnitude and duration of subsequent price swings. Options-based implied volatility indexes (like VIX), Bollinger bands, and Keltner channels enable traders to assess uncertainty levels and manage exposure effectively.
  • Pattern Recognition: Studying recurrent graphical configurations reveals crowd psychology manifestations and illuminates conceivable trajectories. Classifications comprise continuation structures (flags, pennants, wedges), reversal patterns (head & shoulders, double tops/bottoms), and harmonic arrangements (Gartley, Bat, Crab).
  • Intermarket Analysis: Incorporating correlations among distinct assets or sectors deepens comprehension of broader macroeconomic influences shaping supply/demand balances. Cross-referencing equities, commodities, currencies, bonds, and derivatives elucidates linkages and causality effects, improving situational awareness and informing multi-asset allocation frameworks.

A.I.

  • Facebook Prophet is a popular open-source software package developed by Facebook's Core Data Science team for forecasting time series data.
  • TensorFlow Probability: A suite of probabilistic modeling tools built on top of Google's TensorFlow machine learning framework. TensorFlow Probability includes modules for time series analysis, such as state space models and Bayesian structural time series models.
  • PyFlux: A Python library for time series analysis and forecasting, featuring a wide range of models, including ARIMA, exponential smoothing, and dynamic generalized linear models.
  • Linear Regression: A statistical method that models the relationship between two variables by fitting a linear equation to observed data. It is often used as a baseline model in stock price prediction.
  • Decision Trees: A tree-like model of decisions and their possible consequences. It can be used to represent choices, conditions, and outcomes in a way that is easy to understand and interpret.
  • Random Forests: An ensemble learning method that combines multiple decision trees to improve predictive accuracy and reduce overfitting.
  • Support Vector Machines (SVM): A supervised learning algorithm that classifies data points based on their position relative to a hyperplane. SVMs can be used for both regression and classification tasks.
  • Neural Networks: A type of AI model inspired by the structure and function of the human brain. They can learn complex patterns in large datasets and are capable of making accurate predictions.
  • Long Short-Term Memory (LSTM): A type of recurrent neural network (RNN) that is well-suited for time series analysis, such as stock price prediction. LSTMs can remember information for long periods of time, allowing them to capture trends and patterns in historical data.
  • Convolutional Neural Networks (CNN): A type of deep learning algorithm that is typically used for image recognition tasks. However, CNNs can also be adapted for stock price prediction by treating time series data as an "image" with one spatial dimension (time) and one channel (price).
  • Gradient Boosting Machines (GBM): An ensemble learning method that builds multiple decision trees sequentially, where each subsequent tree focuses on correcting the errors made by the previous tree. GBMs are known for their high predictive accuracy and ability to handle nonlinear relationships in the data.
  • Deep Learning: A subset of machine learning that involves training deep neural networks with many layers. These models can learn complex representations of the data and make highly accurate predictions.
  

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