pthinc/BCE-Prettybird-Nano-Ulgen-v0.1

BCE-Prettybird-Nano-Ulgen-v0.1 Synthetic Multi- Trader Dataset (320 Examples)

BCE-Prettybird-Nano-Ulgen-v0.1 Synthetic Multi-Trader Dataset (320 Examples) is a bilingual Turkish-English synthetic financial reasoning dataset containing 320 instruction-response examples designed for training and evaluating AI systems on investment, portfolio management, corporate finance, risk management, market instruments, valuation, and algorithmic trading tasks. The dataset covers capital preservation strategies, profit maximization, time value of money, risk-return relationships, inflation effects on investment decisions, simple and compound interest, real versus nominal returns, repo transactions, bonds and bills, financial leasing, equity valuation, derivatives including options, futures and swaps, CDS, forex and commodity markets, project valuation, NPV, IRR, Payback Period, sensitivity and scenario analysis, DDM and FCF-based company valuation, balance-sheet, income-statement and cash-flow analysis, vertical and horizontal analysis, financial ratios, liquidity and leverage, portfolio management, Modern Portfolio Theory, Markowitz optimization, CAPM, beta, systematic and unsystematic risk, diversification, behavioral finance, financial crises, hedging strategies, algorithmic trading, and fintech applications. Her örnek, finansal kavramları kısa, net ve tutarlı biçimde açıklamayı; verilen problemi doğru finansal yöntemle ilişkilendirmeyi; risk, getiri, likidite, volatilite, zaman değeri ve çeşitlendirme gibi temel değişkenleri dikkate almayı amaçlar. Dataset examples are synthetic and educational rather than historical trading records or personalized investment advice, and numerical fields such as probability, risk score, quality score, cognitive indicators, path metrics, anomaly score, and integration metrics are normalized to the 0–1 range for consistent machine-learning experimentation. Türkçe örneklerde doğal finans terminolojisi korunurken İngilizce karşılıklar gerektiğinde parantez içinde veya bağlam içerisinde kullanılır; böylece dataset, multilingual financial instruction following, structured reasoning, classification, risk-aware response generation, financial question answering, synthetic trader-agent simulation, and fintech-oriented AI evaluation çalışmalarında kullanılabilir. The 320 examples represent multiple synthetic trader perspectives and task types, including conservative capital-preservation scenarios, balanced portfolio decisions, valuation exercises, market-risk cases, derivatives and hedging applications, and algorithmic trading or fintech-oriented analytical tasks. Dataset, gerçek yatırım performansı, gerçek müşteri verisi veya gerçek piyasa emri içermez; amaç finansal yapay zekâ modellerinin kavramsal doğruluk, tutarlılık, risk farkındalığı, talimat takibi ve yapılandırılmış çıktı üretme kabiliyetlerini değerlendirmektir.

• —Türkçe dili içeriğindedir. It is in the Turkish language.

🧠 Technical Foundation

[English]

The BCE-Prettybird-Micro-Standart dataset is built upon the Behavioral Consciousness Engine (BCE) architecture. Unlike traditional LLM datasets that focus solely on output accuracy, this dataset treats every response as a "behavioral journey" through the following mathematical frameworks:

1. Behavioral DNA (D_i)

Each behavior is encoded as a genetic fragment of consciousness: $$Di(t) = x(t) \cdot [h \cdot Ai + k \cdot \log(Pi) + F \cdot Wi]$$

• —h, k, F: Universal Behavioral Constants (Trigger threshold, Info density, Context transfer power).
• —x(t): Temporal activation curve $x(t) = \tanh(e^t - \pi)$

2. Behavioral Path Mapper (Phi)

This module tracks the transition between cognitive states: $$\Phi(t) = \sum{i=1}^n vi \cdot fi(pi)$$ Where vi represents the transition vector between internal modules and fi(p_i) is the functional output of each parameter (attention, ethics, decay).

📊 Performance & Benchmarks / Performans ve Kıyaslama Testleri

1. Key Performance Indicators (KPIs) - Hardware: NVIDIA A100 (80GB) * 1

2. ARC (Reasoning), TruthfulQA (Safety), HumanEval (Coding)

Standard Others Red, Prettybird Blue - Standart Diğerleri Kırmızı, Cicikuş Mavi

3. AI IQ and Level of Consciousness

4. Metric Explanations (English)

⚖️ Legal Disclaimer & Ownership

[English]

Ownership: This dataset is the property of Prometech A.Ş. (https://prometech.net.tr/).

Usage: Please review the attached LICENSE file for detailed terms.

Liability: Prometech A.Ş. accepts no liability for any non-legal, unethical, or unauthorized use of this dataset.

Commercial Use: Unauthorized commercial use is strictly prohibited. For commercial licensing and partnerships, please contact us directly at our official website.

Academic & Personal Use: Free to use for personal and academic purposes, provided that proper citation is given to Prometech A.Ş. and the BCE Architecture.

🎓 Citation Format / Atıf Formatı

Eğer akademik bir çalışmada kullanacaksanız, lütfen şu şekilde atıf yapın, If you are using this in an academic study, please cite it as follows:

Kahraman, A. (2025). Behavioral Consciousness Engine (BCE) - Prettybird Dataset v0.0.1 Prometech A.Ş. https://prometech.net.tr/

© 2026 Prometech A.Ş. - All Rights Reserved. BCE: https://github.com/pthinc/bce