#CurrencyPairPrediction Identifying multiple resistance levels on a Point and Figure chart involves looking for several distinct horizontal columns of Xs at different price levels. These represent areas where upward momentum has been repeatedly stifled. Traders often observe the spacing and relative prominence of these levels. Closely clustered resistance levels might indicate a broader zone of selling interest rather than distinct single points. Conversely, well-separated resistance levels can act as sequential barriers to an advancing price. If the price breaks through a lower resistance level (a shorter or less established X column), the next higher level (a more significant X column) becomes the immediate focus. Analyzing the interplay between these multiple resistance levels can provide a more nuanced understanding of potential price ceilings and areas where selling pressure is likely to re-emerge.

#CurrencyPairPrediction Identifying multiple resistance levels on a Point and Figure chart involves looking for several distinct horizontal columns of Xs at different price levels. These represent areas where upward momentum has been repeatedly stifled. Traders often observe the spacing and relative prominence of these levels. Closely clustered resistance levels might indicate a broader zone of selling interest rather than distinct single points. Conversely, well-separated resistance levels can act as sequential barriers to an advancing price. If the price breaks through a lower resistance level (a shorter or less established X column), the next higher level (a more significant X column) becomes the immediate focus. Analyzing the interplay between these multiple resistance levels can provide a more nuanced understanding of potential price ceilings and areas where selling pressure is likely to re-emerge.

#CurrencyPairPrediction Combining resistance with support on a Point and Figure chart helps define potential trading ranges. Support levels appear as horizontal columns of Os, indicating price levels where buying pressure historically overcame downward trends. When you identify both resistance (X columns) and support (O columns) on the same chart, the area between them can be viewed as a consolidation or trading range. Traders watch for price action within this range, anticipating potential breakouts above resistance or breakdowns below support. The strength and duration of both the resistance and support levels can suggest the significance of the trading range. Understanding this interplay allows traders to develop strategies based on range trading or anticipating the direction of the eventual breakout. A breakout above resistance might signal the start of an uptrend, while a breakdown below support could indicate a downtrend.

#CurrencyPairPrediction Combining resistance with support on a Point and Figure chart helps define potential trading ranges. Support levels appear as horizontal columns of Os, indicating price levels where buying pressure historically overcame downward trends. When you identify both resistance (X columns) and support (O columns) on the same chart, the area between them can be viewed as a consolidation or trading range. Traders watch for price action within this range, anticipating potential breakouts above resistance or breakdowns below support. The strength and duration of both the resistance and support levels can suggest the significance of the trading range. Understanding this interplay allows traders to develop strategies based on range trading or anticipating the direction of the eventual breakout. A breakout above resistance might signal the start of an uptrend, while a breakdown below support could indicate a downtrend.

#CurrencyPairPrediction Confirmation of resistance on a Point and Figure chart occurs after a horizontal column of Xs forms, indicating a price level where selling pressure halted an upward move. The subsequent price action is crucial for validating this level's significance. If, after the X column, a column of Os develops and pushes below the low of the X column, it provides initial confirmation that the selling pressure was indeed strong enough to initiate a downward trend. This subsequent decline reinforces the idea that the high of the X column is likely to act as future resistance. Traders gain more confidence in the resistance level if the price attempts to rally back towards it but stalls or reverses again, forming another X column at or below the previous high. This repeated failure to break through strengthens the conviction that the identified level is a genuine area of resistance.

#CurrencyPairPrediction Confirmation of resistance on a Point and Figure chart occurs after a horizontal column of Xs forms, indicating a price level where selling pressure halted an upward move. The subsequent price action is crucial for validating this level's significance. If, after the X column, a column of Os develops and pushes below the low of the X column, it provides initial confirmation that the selling pressure was indeed strong enough to initiate a downward trend. This subsequent decline reinforces the idea that the high of the X column is likely to act as future resistance. Traders gain more confidence in the resistance level if the price attempts to rally back towards it but stalls or reverses again, forming another X column at or below the previous high. This repeated failure to break through strengthens the conviction that the identified level is a genuine area of resistance.

#CurrencyPairPrediction Challenges in NLP for Finance Challenge Details Domain-Specific Language Finance has unique terms and jargons (“taper tantrum”, “hawkish policy”, etc.). Subtlety A “positive” headline may still be bearish if priced in. Context Same event can have different effects depending on timing. Data Labeling Getting good labeled datasets is hard and expensive. Sarcasm & Hype Especially on social media (e.g., “Yeah right, $TSLA to the moon!”)

#CurrencyPairPrediction Challenges in NLP for Finance Challenge Details Domain-Specific Language Finance has unique terms and jargons (“taper tantrum”, “hawkish policy”, etc.). Subtlety A “positive” headline may still be bearish if priced in. Context Same event can have different effects depending on timing. Data Labeling Getting good labeled datasets is hard and expensive. Sarcasm & Hype Especially on social media (e.g., “Yeah right, $TSLA to the moon!”)

#CurrencyPairPrediction Predictive Modeling Final goal: Use news signals to forecast stock moves. Example: • Build a model that takes news about Apple and predicts whether AAPL stock will go up or down. • Combine news sentiment with price data for better prediction.

#CurrencyPairPrediction Predictive Modeling Final goal: Use news signals to forecast stock moves. Example: • Build a model that takes news about Apple and predicts whether AAPL stock will go up or down. • Combine news sentiment with price data for better prediction.

#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).