Model Training

To ensure the highest level of accuracy, adaptability, and performance, the AI model for Sairi is trained using a combination of advanced methodologies. Each method addresses specific aspects of the system’s functionality, enabling it to deliver a seamless and efficient trading experience.

1. Reinforcement Learning

Reinforcement learning optimizes decision-making by continuously adjusting trading parameters based on rewards and penalties:

• Indicator Threshold Optimization: The RL agent fine-tunes thresholds for indicators like Moving Averages (MA), MACD, and RSI by maximizing cumulative rewards $R_t$ , defined as: $R_t = \sum_{i=t}^{T} \gamma^{(i-t)} r_i$ where $r_t$ is the reward at time $i$ , $\gamma$ is the discount factor ( $0 < \gamma \leq1$ ), and $T$ is the time horizon. Rewards are based on metrics such as profit/loss or reduced risk.

• Pattern Recognition: The agent uses policy optimization (e.g., PPO or DDPG) to identify successful patterns in market data, learning which actions (buy, sell, hold) maximize returns under specific conditions.

2. Deep Learning

Deep learning models analyze historical data to predict trends and user behaviors:

• Price Trend Detection: A convolutional neural network (CNN) processes historical price data $P_t$ , learning to identify patterns such as head-and-shoulders or double tops. The CNN minimizes the loss function: $L = -\frac{1}{N} \sum_{i=1}^{N} \left[ y_i \log(\hat{y}_i) + (1-y_i) \log(1-\hat{y}_i) \right]$ where $y_i$ is the true label (e.g., bullish, bearish), $\hat{y}_i$ is the predicted probability, and $N$ is the number of samples.

• User Preference Modeling: A recurrent neural network (RNN) or long short-term memory (LSTM) network captures sequential patterns in user behaviors, such as trading frequency and preferred assets, enabling personalized strategy recommendations.

3. Bayesian optimization

Bayesian optimization dynamically adjusts strategy parameters to maximize performance:

• Parameter Optimization: Given a black-box objective function $f(\mathbf{x})$ , such as the expected return of a strategy, Bayesian optimization selects the next parameter set $\mathbf{x}{t+1}$ by maximizing the acquisition function $a(\mathbf{x})$ : $\mathbf{x}{t+1} = \arg\max_{\mathbf{x}} a(\mathbf{x}; \mathcal{D}_t)$ where $\mathcal{D}_t$ is the data observed up to iteration $t$ . This ensures continuous refinement of trading strategies based on user feedback and market conditions.

4. Retrieval-Augmented Generation (RAG)

RAG integrates real-time data to ensure timely and context-aware decisions:

• Live Data Retrieval: Queries live market data $D_{\text{real-time}}$ , such as price volatility $\sigma_t$ and interest rates $r_t$ , to update calculations dynamically: $P_{\text{adjusted}} = P_{\text{base}} \cdot (1 + \Delta \sigma_t + \Delta r_t)$ where $P_{\text{adjusted}}$ is the dynamically adjusted price projection.

• Proactive Monitoring: Uses thresholds $T_{\text{price}}$ or $T_{\text{volume}}$ to trigger alerts: $\text{Trigger} = \begin{cases}1 & \text{if } P_t > T_{\text{price}} \text{ or } V_t > T_{\text{volume}} \\0 & \text{otherwise}\end{cases}$

5. Generative Adversarial Networks (GANs)

GANs expand the training dataset and simulate realistic market scenarios:

• Synthetic Data Generation: A GAN consists of a generator $G$ and discriminator $D$ : $\min_G\max_D\mathbb{E}{x \sim p{\text{data}}}[\log D(x)]+\mathbb{E}_{z \sim p_z}[\log(1-D(G(z)))]$ The generator creates synthetic market data $G(z)$ , which the discriminator evaluates for authenticity, ensuring realistic datasets for strategy testing.

• Profit-Loss Simulation: The model simulates trading outcomes using historical constraints, optimizing for maximum returns under varying market assumptions.

6. Natural Language Processing (NLP)

NLP enables effective communication of insights to users:

• Insight Translation: Converts technical data into user-friendly insights using transformer models (e.g., GPT). Example: $\text{Recommendation} = \text{NLP}\big(\text{Price Movement Data} + \text{User Preferences}\big)$

• Notification System: Triggers concise alerts based on predefined rules, such as: $\text{Alert} = \text{if } P_t > T_{\text{profit}} \text{ or } P_t < T_{\text{loss}}$