Emotionics New Feature: Overview of gyo_matrix (v0.6.0) https://pypi.org/project/emotionics/ 1. Overview gyo_matrix is a multi-dimensional observation sensor that concurrently runs simulations across three power gradients (Symmetrical, Upward, and Downward) for a given target text. It outputs the "surface emotion, deep emotion, and structural differences (Delta)" from each respective viewpoint. 2. Core Philosophy • Complete Elimination of Quantization (No Quantization) • It intentionally avoids calculating single scores or metrics like "Anger Level 80%" or "Threat Level 0.85." • This prevents the "number bias" where humans blindly trust system-generated figures, ensuring the tool does not devolve into a system that unilaterally labels, judges, or intervenes with the subject. • Role as a Pure Sensor • The system merely presents the "structural contradictions" within the three gravitational fields (power gradients). • Compari...

ドル円アノマリー追記：データが暴く「公務員の給料」も刈り取られる日 前回の記事では、「毎月25日の給料日」を起点としたシステム的な円売り・ドル買いアノマリーについて解説しました。今回はさらに解像度を上げ、「毎月1日〜31日の日付別」でドル円の平均変化率（リターン）を可視化してみました。 使用したPythonコードは以下の通りです。 ‘’’Python import yfinance as yf import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # 分析期間の設定 dateStart = "2015-01-01" dateEnd   = "2026-05-23" ticker = "JPY=X"   # ドル円 df = yf.download(     ticker,     start=dateStart,     end=dateEnd,     auto_adjust=False,     progress=False ) if isinstance(df.columns, pd.MultiIndex):     close = df["Close"][ticker].dropna() else:     close = df["Close"].dropna() daily_return = close.pct_change() * 100 analysis_df = pd.DataFrame({"Return": daily_return}) analysis_df.index = pd.to_datetime(analysis_df.index) analysis_df["Day_of_Month"] = analysis_df.index.day analysis_df = analysis_df[analysis_df.index.dayofweek < 5] pivot_table = analysis_df.groupby("Day_of_Month")["Return...

Visualizing Japan's Capital Flight: The "Payday Anomaly" in USD/JPY The Japanese Yen has been experiencing historic weakness. While many attribute this solely to interest rate differentials or government interventions, I suspected a more mechanical, structural force at play: the automated capital flight by Japanese retail investors. The Hypothesis: The Payday Effect In Japan, the 25th of the month is the standard payday for most corporate workers. Recently, due to inflation and the new tax-free investment program (NISA), a massive number of people have set up automated monthly purchases of foreign equity index funds (such as the S&P 500 or All-Country World Index). I hypothesized that this creates an automatic, system-wide "Sell JPY / Buy USD" order triggered every single month around payday. The Code To verify this, I wrote a Python script using yfinance and seaborn to map the average daily return of USD/JPY by the week and day of the month, covering data...