The Economic Bomb: A Strategic Financial Warfare Tactic
Description
This dataset provides evidence supporting the hypothesis that institutional shorting, ETF outflows, whale wallet movements, and media sentiment drive Bitcoin’s volatility and price manipulation. Central to this dataset is the Decker Sentiment-Short Interest Model (DSSIM)—an original equation developed by Nicolin Decker to quantify the relationship between market sentiment and institutional short interest. By combining sentiment scores from Natural Language Processing (NLP) and short positioning data, DSSIM offers a flexible framework for analyzing volatility in Bitcoin and other assets. The dataset spans January 2021 to December 2024, capturing daily market activity and key price events. Each file aligns with DSSIM’s variables, enabling replication and further analysis of the findings in the doctoral-level thesis The Economic Bomb: A Strategic Financial Warfare Tactic. Key Components: BTC_Price_Data.csv: Daily BTC/USD closing prices from Binance, Coinbase, and Bitstamp, serving as the baseline for volatility and return calculations. ETF_Holdings_Over_Time_Thesis.csv: Daily BTC holdings of ETFs (Grayscale, BlackRock, and Fidelity), illustrating cumulative outflows and their liquidity impact. ETF_Outflows_Price_Impact_Data.csv: Correlates ETF outflows with BTC volatility, highlighting timing and magnitude. Institutional_Shorting_Data.csv: Daily BTC short positions from Binance, BitMEX, Bybit, and OKX, serving as input for DSSIM’s short interest variable. Whale_Wallet_Movements.csv: Tracks large BTC wallet movements, revealing sell-offs preceding price crashes and influencing DSSIM’s residual noise component. Market_Liquidity_Data.csv: Daily BTC trading volume, order book depth, and liquidity ratios, validating DSSIM’s predictive capabilities. Media_Sentiment_Scores.csv: Daily sentiment from Twitter, Reddit, Google News, and YouTube, forming DSSIM’s sentiment variable. Monte_Carlo_Simulation_Results.csv: Simulates 1,000 BTC price paths to assess potential volatility under market stress. VAR_Model_Data.csv: Analyzes ETF outflows’ delayed impact on BTC returns using vector autoregression. Volatility_Clustering_Data.csv: Tracks daily BTC returns and 30-day rolling volatility, confirming persistent volatility after institutional actions. GARCH_Model_Data.csv: Models BTC volatility using GARCH, validating volatility clustering during market shocks. The dataset includes adjustments for major market events, such as the May 2021 Flash Crash, June 2022 Liquidation Crisis, and March 2023 Banking Crisis, ensuring realistic volatility patterns aligned with DSSIM’s modeling of sentiment shifts and institutional shorting. Researchers can use DSSIM’s structure and data to explore similar dynamics in other cryptocurrencies, equities, commodities, and forex markets, advancing financial analysis and predictive modeling. Access the full dataset: https://drive.google.com/drive/folders/1pnwqBTMF_QSJoC5QcNAPSQpVtOST2n8c?usp=drive_link
Files
Steps to reproduce
The datasets were collected, processed, and analyzed using Python scripts, statistical models, and data sourced from leading cryptocurrency exchanges, analytics platforms, and social media networks. 1. Data Collection - Use the provided `data_collection.py` script to collect raw data from APIs, including Binance, Coinbase, Bitstamp, Glassnode, Twitter, Reddit, Google News, and YouTube. - Ensure API keys are correctly configured in the script to enable data extraction. - Store the collected data in the `/Raw_Data` directory as CSV files. 2. Data Preprocessing - Run the `data_preprocessing.py` script to clean and format the raw data into structured datasets. - Apply event-driven adjustments for key market events such as the May 2021 Flash Crash, June 2022 Liquidation Crisis, and March 2023 Banking Crisis to ensure realistic volatility patterns. - Output the cleaned datasets to the `/Processed_Data` directory. 3. Statistical Modeling - Use the `GARCH_model.ipynb` script to estimate BTC volatility and residuals using the GARCH framework. - Run the `VAR_model.ipynb` script to analyze the time-lagged impact of ETF outflows on BTC returns using the vector autoregression (VAR) model. - Execute the `monte_carlo_simulation.py` script to simulate 1,000 BTC price paths using historical volatility, capturing potential downside risks. 4. Data Analysis and Visualization - Generate visualizations using Python libraries (Matplotlib, Pandas) to illustrate ETF holdings, outflows, whale wallet movements, and volatility clustering. - Overlay major market events to highlight their impact on BTC volatility and liquidity. 5. Replication and Verification - Use the final processed datasets to replicate the statistical analyses and validate the thesis’s core hypothesis: that coordinated institutional actions and media sentiment collectively drive BTC volatility and price manipulation. - Compare simulation results and VAR model predictions against actual BTC price movements during key events. Full Instructions for Reproduction Detailed step-by-step instructions, including Python code, data collection procedures, and statistical modeling, are available in the public Google Drive folder: 📁 [Access the Full Reproduction Guide] https://drive.google.com/file/d/1SPjUwAKcqz-1OXR6qM3WPtWR6mAnv5mj/view?usp=sharing