#CurrencyPairPrediction Topic Modeling Discover hidden themes/topics in large news corpora. Tools: • LDA (Latent Dirichlet Allocation): Find groups like “M&A news”, “Earnings reports”, “Regulation risk” • Non-negative Matrix Factorization (NMF) This helps cluster news into categories, prioritize focus, or detect trends.

#CurrencyPairPrediction Topic Modeling Discover hidden themes/topics in large news corpora. Tools: • LDA (Latent Dirichlet Allocation): Find groups like “M&A news”, “Earnings reports”, “Regulation risk” • Non-negative Matrix Factorization (NMF) This helps cluster news into categories, prioritize focus, or detect trends.

#CurrencyPairPrediction Event Extraction Identify specific events from news: • “Apple announces stock split” • “Tesla recalls vehicles” • “Fed raises interest rates” This involves: • Entity recognition (who, what) • Relation extraction (what happened to whom) Structured event data can be fed into trading algorithms.

#CurrencyPairPrediction Event Extraction Identify specific events from news: • “Apple announces stock split” • “Tesla recalls vehicles” • “Fed raises interest rates” This involves: • Entity recognition (who, what) • Relation extraction (what happened to whom) Structured event data can be fed into trading algorithms.

#CurrencyPairPrediction Sentiment Analysis Key application in finance. Goal: Decide if the news is Positive, Negative, or Neutral for a stock/market. Methods: • Lexicon-Based: Use predefined dictionaries (e.g., Loughran-McDonald Finance Sentiment Dictionary) • Machine Learning-Based: Train classifiers (SVMs, XGBoost) on labeled financial news • Deep Learning-Based: Use transformers like FinBERT, trained specifically on financial text. Example Output: News: “Amazon’s revenue beats Wall Street estimates.” Sentiment: Positive

#CurrencyPairPrediction Sentiment Analysis Key application in finance. Goal: Decide if the news is Positive, Negative, or Neutral for a stock/market. Methods: • Lexicon-Based: Use predefined dictionaries (e.g., Loughran-McDonald Finance Sentiment Dictionary) • Machine Learning-Based: Train classifiers (SVMs, XGBoost) on labeled financial news • Deep Learning-Based: Use transformers like FinBERT, trained specifically on financial text. Example Output: News: “Amazon’s revenue beats Wall Street estimates.” Sentiment: Positive

#CurrencyPairPrediction Feature Extraction (Vectorization) Convert text into numbers so ML models can understand it: • Bag of Words (BoW): Simple word counts • TF-IDF (Term Frequency-Inverse Document Frequency): Weigh rare but important words higher • Word Embeddings: • Word2Vec, GloVe (pre-trained vector spaces) • Finance-specific embeddings (e.g., FinBERT) Embeddings capture semantic meaning: (e.g., “profit” and “earnings” are close in the vector space).

#CurrencyPairPrediction Feature Extraction (Vectorization) Convert text into numbers so ML models can understand it: • Bag of Words (BoW): Simple word counts • TF-IDF (Term Frequency-Inverse Document Frequency): Weigh rare but important words higher • Word Embeddings: • Word2Vec, GloVe (pre-trained vector spaces) • Finance-specific embeddings (e.g., FinBERT) Embeddings capture semantic meaning: (e.g., “profit” and “earnings” are close in the vector space).

#CurrencyPairPrediction Defining Box Size and Its Impact: The box size is a critical parameter in Point and Figure charting, representing the price increment required to add an X or an O to a column. A smaller box size makes the chart more sensitive to price fluctuations, resulting in more columns and potentially earlier signals, but also increased noise. Conversely, a larger box size filters out more minor price movements, leading to fewer columns and potentially lagging signals, but providing a smoother view of significant trends. The choice of box size depends on the asset's volatility and the trader's time horizon; more volatile assets or shorter-term trading may benefit from smaller box sizes, while less volatile assets or longer-term analysis might use larger sizes to focus on substantial price changes.

#CurrencyPairPrediction Defining Box Size and Its Impact: The box size is a critical parameter in Point and Figure charting, representing the price increment required to add an X or an O to a column. A smaller box size makes the chart more sensitive to price fluctuations, resulting in more columns and potentially earlier signals, but also increased noise. Conversely, a larger box size filters out more minor price movements, leading to fewer columns and potentially lagging signals, but providing a smoother view of significant trends. The choice of box size depends on the asset's volatility and the trader's time horizon; more volatile assets or shorter-term trading may benefit from smaller box sizes, while less volatile assets or longer-term analysis might use larger sizes to focus on substantial price changes.

#CurrencyPairPrediction Preprocess the Text Raw text is messy — preprocessing cleans it up: • Tokenization: Split text into words • Lowercasing: Normalize case • Removing Stopwords: Words like “the”, “is”, “and” • Stemming / Lemmatization: Reduce words to base form (e.g., “running” -> “run”) • Named Entity Recognition (NER): Identify companies, locations, monetary amounts • Handling special financial entities: e.g., recognizing ticker symbols like “AAPL”, “TSLA” Example: Original: “Tesla Inc. reported a 20% jump in Q1 revenue!” Preprocessed: [‘tesla’, ‘inc’, ‘report’, ‘20%’, ‘jump’, ‘q1’, ‘revenue’]

#CurrencyPairPrediction Preprocess the Text Raw text is messy — preprocessing cleans it up: • Tokenization: Split text into words • Lowercasing: Normalize case • Removing Stopwords: Words like “the”, “is”, “and” • Stemming / Lemmatization: Reduce words to base form (e.g., “running” -> “run”) • Named Entity Recognition (NER): Identify companies, locations, monetary amounts • Handling special financial entities: e.g., recognizing ticker symbols like “AAPL”, “TSLA” Example: Original: “Tesla Inc. reported a 20% jump in Q1 revenue!” Preprocessed: [‘tesla’, ‘inc’, ‘report’, ‘20%’, ‘jump’, ‘q1’, ‘revenue’]

#CurrencyPairPrediction Identifying Resistance Levels on Point and Figure Charts: Resistance levels appear as horizontal columns of Xs on a Point and Figure chart. These columns represent price levels where selling pressure has historically been strong enough to stop an upward trend and potentially push prices lower. Once a column of Xs is established and the price subsequently falls, the low of that X column often acts as a future resistance level. Traders observe these X columns; if the price rallies back to this level and stalls, it can suggest a potential selling opportunity, expecting another move downward. The more prominent and extended the horizontal column of Xs, the stronger the indicated resistance level.

#CurrencyPairPrediction Identifying Resistance Levels on Point and Figure Charts: Resistance levels appear as horizontal columns of Xs on a Point and Figure chart. These columns represent price levels where selling pressure has historically been strong enough to stop an upward trend and potentially push prices lower. Once a column of Xs is established and the price subsequently falls, the low of that X column often acts as a future resistance level. Traders observe these X columns; if the price rallies back to this level and stalls, it can suggest a potential selling opportunity, expecting another move downward. The more prominent and extended the horizontal column of Xs, the stronger the indicated resistance level.

#CurrencyPairPrediction How NLP is Applied to Financial News Here’s the usual flow: ⸻ Collect Financial Text Data Sources: • News articles (Bloomberg, Reuters, CNBC) • Press releases • Earnings transcripts • Tweets, Reddit posts • Regulatory filings (e.g., SEC 10-K, 10-Q reports)

#CurrencyPairPrediction How NLP is Applied to Financial News Here’s the usual flow: ⸻ Collect Financial Text Data Sources: • News articles (Bloomberg, Reuters, CNBC) • Press releases • Earnings transcripts • Tweets, Reddit posts • Regulatory filings (e.g., SEC 10-K, 10-Q reports)

#CurrencyPairPrediction Why Use NLP in Financial News Analysis? Financial markets are highly sensitive to news: • Company earnings reports • Economic announcements • Geopolitical events • Rumors, mergers, lawsuits, etc. Human traders can’t read thousands of news articles fast enough — but machines can: • Process huge volumes of news in real-time • Extract meaningful signals (positive/negative impact) • Help make faster and often better-informed trading decisions Thus, NLP becomes a trading edge.

#CurrencyPairPrediction Why Use NLP in Financial News Analysis? Financial markets are highly sensitive to news: • Company earnings reports • Economic announcements • Geopolitical events • Rumors, mergers, lawsuits, etc. Human traders can’t read thousands of news articles fast enough — but machines can: • Process huge volumes of news in real-time • Extract meaningful signals (positive/negative impact) • Help make faster and often better-informed trading decisions Thus, NLP becomes a trading edge.

#CurrencyPairPrediction Construction of Point and Figure Charts: These charts are built with columns of Xs representing rising prices and columns of Os indicating falling prices. The process begins by defining a "box size," which is a specific price increment. For every upward movement equal to the box size, an X is added to the current column. Conversely, for every downward movement of the same magnitude, an O is added to its column. Crucially, a new column is only initiated when the price reverses direction by a predetermined multiple of the box size, known as the "reversal criteria" (commonly 3 times the box size). For instance, if the price has been rising (forming a column of Xs), a new column of Os will only start once the price drops by at least three box sizes. This mechanism ensures that minor price oscillations are ignored, and only substantial directional shifts are recorded, providing a clear visual representation of significant price trends without the influence of time.

#CurrencyPairPrediction Construction of Point and Figure Charts: These charts are built with columns of Xs representing rising prices and columns of Os indicating falling prices. The process begins by defining a "box size," which is a specific price increment. For every upward movement equal to the box size, an X is added to the current column. Conversely, for every downward movement of the same magnitude, an O is added to its column. Crucially, a new column is only initiated when the price reverses direction by a predetermined multiple of the box size, known as the "reversal criteria" (commonly 3 times the box size). For instance, if the price has been rising (forming a column of Xs), a new column of Os will only start once the price drops by at least three box sizes. This mechanism ensures that minor price oscillations are ignored, and only substantial directional shifts are recorded, providing a clear visual representation of significant price trends without the influence of time.

#CurrencyPairPrediction What is NLP (Natural Language Processing)? NLP is a field of Artificial Intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. In the context of finance, NLP helps systems: • Read news articles • Analyze social media posts • Understand earnings calls, reports, tweets • Detect sentiments, topics, risks, opportunities

#CurrencyPairPrediction What is NLP (Natural Language Processing)? NLP is a field of Artificial Intelligence (AI) that focuses on enabling computers to understand, interpret, and generate human language. In the context of finance, NLP helps systems: • Read news articles • Analyze social media posts • Understand earnings calls, reports, tweets • Detect sentiments, topics, risks, opportunities

#CurrencyPairPrediction Popular Libraries for RL in Trading • Stable-Baselines3 (Python) • Ray RLlib (scalable RL) • FinRL (RL library specifically for trading) • TensorTrade (modular RL framework for trading) Final Thought: RL-based trading strategies are powerful, adaptive, and intelligent — but they’re also high-risk and require careful design and continuous updating to work reliably in real-world markets.

#CurrencyPairPrediction Popular Libraries for RL in Trading • Stable-Baselines3 (Python) • Ray RLlib (scalable RL) • FinRL (RL library specifically for trading) • TensorTrade (modular RL framework for trading) Final Thought: RL-based trading strategies are powerful, adaptive, and intelligent — but they’re also high-risk and require careful design and continuous updating to work reliably in real-world markets.

#CurrencyPairPrediction Core Principles of Point and Figure Charting: This method focuses exclusively on significant price movements, disregarding the element of time. Instead of plotting price against a time axis, it tracks substantial upward and downward price changes using columns of Xs and Os, respectively. A new column only appears when the price reverses direction by a predetermined amount (the box size), effectively filtering out smaller, time-bound fluctuations to highlight meaningful price trends.

#CurrencyPairPrediction Core Principles of Point and Figure Charting: This method focuses exclusively on significant price movements, disregarding the element of time. Instead of plotting price against a time axis, it tracks substantial upward and downward price changes using columns of Xs and Os, respectively. A new column only appears when the price reverses direction by a predetermined amount (the box size), effectively filtering out smaller, time-bound fluctuations to highlight meaningful price trends.

#CurrencyPairPrediction Example Workflow Step Description 1 Collect historical data (price, volume, indicators) 2 Define state, action, reward structure 3 Choose and implement an RL algorithm (say, PPO) 4 Train the agent on the data (using simulations) 5 Validate performance on unseen data 6 Deploy to live (or paper) trading with careful monitoring

#CurrencyPairPrediction Example Workflow Step Description 1 Collect historical data (price, volume, indicators) 2 Define state, action, reward structure 3 Choose and implement an RL algorithm (say, PPO) 4 Train the agent on the data (using simulations) 5 Validate performance on unseen data 6 Deploy to live (or paper) trading with careful monitoring

#CurrencyPairPrediction Advanced Concepts • Risk-Sensitive RL: Not just maximizing profit, but adjusting actions based on risk appetite. • Meta-RL / Online Learning: Adapting even faster to new market conditions. • Multi-Agent RL: Simulate multiple trading agents competing/collaborating to model real-world dynamics. • Hierarchical RL: High-level strategy (trend following) vs. low-level tactics (exact timing).

#CurrencyPairPrediction Advanced Concepts • Risk-Sensitive RL: Not just maximizing profit, but adjusting actions based on risk appetite. • Meta-RL / Online Learning: Adapting even faster to new market conditions. • Multi-Agent RL: Simulate multiple trading agents competing/collaborating to model real-world dynamics. • Hierarchical RL: High-level strategy (trend following) vs. low-level tactics (exact timing).

#CurrencyPairPrediction Point and Figure charts are a price-based charting method that filters out the element of time. Instead of plotting price against time, these charts only record significant price movements. They are constructed using columns of Xs to represent rising prices and columns of Os to represent falling prices. A new column is only started when the price moves by a predetermined box size in the opposite direction. Traders often use Point and Figure charts to identify clear support and resistance levels, as well as classic chart patterns like double tops, double bottoms, and triangles. Breakouts above established columns of Xs or below columns of Os can signal potential buying or selling opportunities. Because time is not a factor, these charts can be particularly useful for identifying long-term trends and filtering out short-term noise. They can also help in setting price targets based on the height of the patterns formed. The focus on significant price changes makes Point and Figure charts a distinct approach compared to time-based charts like candlestick or line charts.

#CurrencyPairPrediction Point and Figure charts are a price-based charting method that filters out the element of time. Instead of plotting price against time, these charts only record significant price movements. They are constructed using columns of Xs to represent rising prices and columns of Os to represent falling prices. A new column is only started when the price moves by a predetermined box size in the opposite direction. Traders often use Point and Figure charts to identify clear support and resistance levels, as well as classic chart patterns like double tops, double bottoms, and triangles. Breakouts above established columns of Xs or below columns of Os can signal potential buying or selling opportunities. Because time is not a factor, these charts can be particularly useful for identifying long-term trends and filtering out short-term noise. They can also help in setting price targets based on the height of the patterns formed. The focus on significant price changes makes Point and Figure charts a distinct approach compared to time-based charts like candlestick or line charts.

#CurrencyPairPrediction Challenges in RL for Trading (a) Noisy Data Markets are influenced by unpredictable events (news, politics). (b) Non-Stationary Environment Market patterns change over time (e.g., COVID crash vs. bull market). (c) Risk of Overfitting Training only on historical data might make the model too “optimistic” about the past — may not generalize to future data. (d) Reward Design Simple profit might not be enough — need to balance risk, transaction costs, slippage.

#CurrencyPairPrediction Challenges in RL for Trading (a) Noisy Data Markets are influenced by unpredictable events (news, politics). (b) Non-Stationary Environment Market patterns change over time (e.g., COVID crash vs. bull market). (c) Risk of Overfitting Training only on historical data might make the model too “optimistic” about the past — may not generalize to future data. (d) Reward Design Simple profit might not be enough — need to balance risk, transaction costs, slippage.

#CurrencyPairPrediction Harmonic patterns are geometric price structures that traders believe can predict future price movements with high probability. These patterns are based on specific Fibonacci retracement and extension levels, creating distinct shapes like Gartley, Butterfly, Bat, and Crab patterns. The underlying principle is that these patterns represent points of potential reversal in the market. Identifying harmonic patterns involves recognizing specific price swings and measuring the relationships between these swings using Fibonacci ratios. For example, a Gartley pattern has specific Fibonacci retracement levels that must be met at certain points within the four-leg structure (X-A, A-B, B-C, and C-D). When these Fibonacci ratios align correctly, the point D is considered a Potential Reversal Zone (PRZ). Traders often look for confluence of Fibonacci levels from different swings within the pattern to increase the reliability of the PRZ. Once the price enters the PRZ, traders watch for confirmation signals from other technical indicators before entering a trade. Harmonic patterns are favored by some traders for their precise entry and exit points and their reliance on Fibonacci geometry.

#CurrencyPairPrediction Harmonic patterns are geometric price structures that traders believe can predict future price movements with high probability. These patterns are based on specific Fibonacci retracement and extension levels, creating distinct shapes like Gartley, Butterfly, Bat, and Crab patterns. The underlying principle is that these patterns represent points of potential reversal in the market. Identifying harmonic patterns involves recognizing specific price swings and measuring the relationships between these swings using Fibonacci ratios. For example, a Gartley pattern has specific Fibonacci retracement levels that must be met at certain points within the four-leg structure (X-A, A-B, B-C, and C-D). When these Fibonacci ratios align correctly, the point D is considered a Potential Reversal Zone (PRZ). Traders often look for confluence of Fibonacci levels from different swings within the pattern to increase the reliability of the PRZ. Once the price enters the PRZ, traders watch for confirmation signals from other technical indicators before entering a trade. Harmonic patterns are favored by some traders for their precise entry and exit points and their reliance on Fibonacci geometry.

#CurrencyPairPrediction Train the Agent • Simulate trading over historical data (“backtesting”). • Agent makes decisions, receives feedback (reward), updates its policy. • Optionally simulate live trading in a paper trading environment.

#CurrencyPairPrediction Train the Agent • Simulate trading over historical data (“backtesting”). • Agent makes decisions, receives feedback (reward), updates its policy. • Optionally simulate live trading in a paper trading environment.

#CurrencyPairPrediction Choose an RL Algorithm Some popular RL methods used: • Q-Learning (simpler, good for small discrete action spaces) • Deep Q-Networks (DQN) (using deep learning to handle complex data) • Policy Gradient Methods (like REINFORCE) • Actor-Critic Methods (like A2C, PPO, DDPG) — very popular in finance • Proximal Policy Optimization (PPO) — very stable, handles continuous actions well

#CurrencyPairPrediction Choose an RL Algorithm Some popular RL methods used: • Q-Learning (simpler, good for small discrete action spaces) • Deep Q-Networks (DQN) (using deep learning to handle complex data) • Policy Gradient Methods (like REINFORCE) • Actor-Critic Methods (like A2C, PPO, DDPG) — very popular in finance • Proximal Policy Optimization (PPO) — very stable, handles continuous actions well