An intelligent risk control analysis system and method for futures and options

Abstract

The present application relates to a kind of intelligent futures and options fusion risk control analysis system and method, method includes: risk factor identification module real-time analysis collected data, identify the main risk factor that current influence market, and pass through AI risk prediction engine operation prediction model;Risk prediction index is calculated, and Monte Carlo simulation method is used to evaluate futures and options combination risk;The exposure of each risk dimension is calculated, risk exposure analysis is carried out, and key indicators are monitored in real time to give early warning;When triggering early warning, automatically execute emergency response.The present application realizes the combination risk evaluation of futures and options fusion, the combination risk of futures position and spot position is calculated by Monte Carlo simulation method, accurately assesses hedging effect, avoids the risk underestimation or overestimation problem caused by separation evaluation.Test shows that the VaR accuracy of combination risk evaluation is improved by 35% than separation evaluation, and effective risk capital occupancy is reduced by 20%.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to an intelligent futures-spot fusion risk control and analysis system and method. Background Technology

[0002] In the commodity trading sector, companies participate in both futures and spot market transactions, facing complex and volatile market risks. The futures market is characterized by high leverage and dramatic price fluctuations, requiring strict margin management and risk control. The spot market involves various risk types, including inventory risk, credit risk, and logistical risk. Traditional risk management methods rely primarily on human experience and simple early warning indicators, which are insufficient to address the complex risks arising from the linkage between the futures and spot markets. With the development of financial technology, risk management systems are increasingly incorporating quantitative analysis methods, such as VaR (Value at Risk) models and stress testing tools. However, most existing risk management systems are designed separately for futures or spot transactions, lacking the ability to assess the overall risk of integrated futures and spot transactions. Particularly in complex business scenarios such as hedging, basis trading, and futures-spot arbitrage, complex hedging relationships exist between futures and spot positions. Traditional risk control systems struggle to accurately assess portfolio risk, potentially leading to underestimation or over-conservatism. Furthermore, the commodity market is influenced by various factors such as macroeconomic conditions, supply and demand, policy changes, and unforeseen events, resulting in non-linear, multi-periodic, and abrupt price fluctuations that traditional linear statistical models struggle to capture. Artificial intelligence technologies, especially deep learning and graph neural networks, have provided new tools for risk prediction and management, but their application in the field of commodity risk control is not yet deep enough.

[0003] Currently, enterprises face the following main problems in commodity risk management: 1. Separation and assessment of futures and spot risks: Existing risk control systems typically calculate futures and spot risks separately, failing to accurately assess the hedging effect of futures and spot positions. This leads to inaccurate risk measurement, potentially resulting in underestimation of actual risk or excessive use of risk capital, impacting capital utilization efficiency and business decisions. 2. Incomplete identification of risk factors: Traditional risk control systems primarily focus on price and market risks, lacking sufficient ability to identify and quantify non-market risks such as credit risk, liquidity risk, operational risk, and policy risk. This makes it difficult to construct a comprehensive risk profile and may lead to the omission of important risk points. 3. Limited risk prediction models: Existing systems often employ historical statistical methods such as moving averages and GARCH models. These methods assume the market follows a specific distribution, making it difficult to capture extreme events and nonlinear relationships. Prediction accuracy drops significantly during periods of sharp market fluctuations, failing to provide effective early warnings. 4. Insufficient real-time monitoring capabilities: Risk data is updated infrequently (usually daily or weekly), failing to reflect market changes in real time, especially during periods of rapid price fluctuations. 5. In the event of an emergency, the risk control system reacts slowly, potentially missing the optimal time for risk management and causing further losses; 6. The stress testing scenarios are limited. Existing systems primarily conduct stress tests based on historical extreme situations, making it difficult to simulate complex multi-factor linkage scenarios and unprecedented black swan events. The results of stress tests have limited guiding significance for actual risk management; 7. Risk limit management is rigid. Risk limits are usually set according to fixed percentages or absolute values, lacking the ability to dynamically adjust based on market conditions, position structure, and business type. This can be overly conservative and restrict business development when the market is stable, while potentially being overly relaxed during market fluctuations; 8. Risk attribution analysis is crude. When risk losses occur, existing systems struggle to accurately attribute them to specific risk factors, trading decisions, or business processes, failing to provide effective evidence for risk management improvement and leading to the recurrence of similar risks; 9. There is a lack of monitoring for cross-market risk contagion. Complex relationships exist between the futures market, spot market, and financial market. Risk in one market can spread to other markets. Existing systems lack the ability to monitor and warn of cross-market risk contagion, potentially leading to systemic risks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent futures-spot fusion risk control and analysis system and method, which solves the deficiencies of the prior art.

[0005] The objective of this invention is achieved through the following technical solution: an intelligent futures-spot fusion risk control and analysis system, the system comprising: a multi-source data acquisition module, a risk factor identification module, an AI risk prediction engine, a futures-spot combination risk assessment module, and a real-time monitoring and early warning module;

[0006] The multi-source data acquisition module is configured to collect futures market data, spot market data, macroeconomic data, industry data, news and public opinion data, and policy and regulatory data in real time.

[0007] The risk factor identification module is configured to automatically identify key factors affecting price fluctuations from the collected data and build a risk factor library.

[0008] The AI ​​risk prediction engine is configured to calculate risk indicators based on deep learning algorithms and generate risk prediction reports at multiple time scales.

[0009] The futures-spot portfolio risk assessment module is configured to use Monte Carlo simulation to comprehensively assess the portfolio risk of futures and spot positions, calculate risk metrics, and identify risk exposures and hedging gaps.

[0010] The real-time monitoring and early warning module is configured to monitor key risk indicators in real time, and automatically trigger tiered early warnings and notify relevant personnel when the indicators exceed the early warning threshold.

[0011] The system also includes a stress testing module, a risk limit management module, a risk attribution analysis module, and a visualization platform;

[0012] The stress testing module is configured to stress test the portfolio based on historical scenarios, hypothetical scenarios, and extreme scenarios, assess potential losses under extreme market conditions, and provide risk response recommendations.

[0013] The risk limit management module is configured to dynamically set and adjust risk limits based on the company's risk appetite, capital adequacy ratio, and market conditions, and to monitor the use of the limits in real time.

[0014] The risk attribution analysis module is configured to decompose risk gains and losses into specific risk factors, market variables, and trading strategies, and identify the main sources of risk.

[0015] The visualization platform is configured to provide various display formats, including risk heatmaps, risk trend charts, and stress test reports.

[0016] The AI ​​risk prediction engine specifically performs the following:

[0017] A1. Standardize the original data and construct a feature vector X_t = [P_f, P_s, V, OI, B, σ, macroeconomic indicators, industry indicators, public opinion indicators], where P_f is the futures contract price, P_s is the spot price, V is the trading volume, OI is the open interest, B is the basis, and σ is the volatility.

[0018] A2. Input the feature sequence {X_t-59, ..., X_t} of the past 60 trading days into the LSTM network, extract the time series pattern, and output the hidden state h_t;

[0019] A3. Employ a self-attention mechanism to calculate the correlation weights between different time points and capture long-term relationships;

[0020] A4. Generate price distribution predictions P_{t+k} and volatility predictions σ_{t+k} for the next 5, 10, and 20 days, as well as the probability of extreme events P(extreme).

[0021] The aforementioned futures-spot portfolio risk assessment module uses Monte Carlo simulation to comprehensively assess the combined risk of futures and spot positions, and calculates risk metrics including:

[0022] B1. Get current positions: Futures long positions Q_f, spot inventory Q_s, convert to a uniform unit;

[0023] B2. Generate random paths: Based on the predicted price distribution, generate N price simulation paths {P^(i)_{t+k}}, i=1...N;

[0024] B3. Calculate the profit and loss for each path: Futures profit and loss = Q_f × contract multiplier × (P^(i)_{t+k} - P_t), Spot profit and loss = Q_s × (P^(i)_{s,t+k} - P_{s,t}), Total profit and loss = Futures profit and loss + Spot profit and loss;

[0025] B4. Calculate risk indicators: VaR(95%) = 5th percentile loss value, CVaR(95%) = average loss value below VaR, maximum drawdown = max(cumulative maximum return - current return).

[0026] A method based on an intelligent futures-spot fusion risk control and analysis system, the method comprising:

[0027] S1, the risk factor identification module analyzes the collected data in real time, identifies the main risk factors currently affecting the market, and runs a prediction model through the AI ​​risk prediction engine;

[0028] S2. Calculate risk prediction indicators and use Monte Carlo simulation method to assess the risk of futures-spot portfolio;

[0029] S3. Calculate the exposure of each risk dimension, conduct risk exposure analysis, and monitor key indicators in real time for early warning;

[0030] S4. When an alert is triggered, an emergency response will be automatically executed.

[0031] The operation of the prediction model via the AI ​​risk prediction engine includes:

[0032] A1. Standardize the original data and construct a feature vector X_t = [P_f, P_s, V, OI, B, σ, macroeconomic indicators, industry indicators, public opinion indicators], where P_f is the futures contract price, P_s is the spot price, V is the trading volume, OI is the open interest, B is the basis, and σ is the volatility.

[0033] A2. Input the feature sequence {X_t-59, ..., X_t} of the past 60 trading days into the LSTM network, extract the time series pattern, and output the hidden state h_t;

[0034] A3. Employ a self-attention mechanism to calculate the correlation weights between different time points and capture long-term relationships;

[0035] A4. Generate price distribution predictions P̂_{t+k} and volatility predictions σ̂_{t+k} for the next 5, 10, and 20 days, as well as the probability of extreme events P(extreme).

[0036] The content of assessing the risk of futures-spot portfolios using the Monte Carlo simulation method includes:

[0037] B1. Get current positions: Futures long positions Q_f, spot inventory Q_s, convert to a uniform unit;

[0038] B2. Generate random paths: Based on the predicted price distribution, generate N price simulation paths {P^(i)_{t+k}}, i=1...N;

[0039] B3. Calculate the profit and loss for each path: Futures profit and loss = Q_f × contract multiplier × (P^(i)_{t+k} - P_t), Spot profit and loss = Q_s × (P^(i)_{s,t+k} - P_{s,t}), Total profit and loss = Futures profit and loss + Spot profit and loss;

[0040] B4. Calculate risk indicators: VaR(95%) = 5th percentile loss value, CVaR(95%) = average loss value below VaR, maximum drawdown = max(cumulative maximum return - current return).

[0041] The calculation of exposure for each risk dimension and the risk exposure analysis include:

[0042] C1, Price Risk Exposure = |Q_f × Contract Multiplier + Q_s| × P_t × σ, represents the potential loss of 1% price fluctuation;

[0043] C2, Basis risk exposure = |Q_f × contract multiplier Q_s| × |B| / P_s, representing the exposure to basis changes;

[0044] C3. Hedging ratio = Q_f × contract multiplier / Q_s, with the ideal hedging ratio being close to 1.

[0045] The method further includes:

[0046] S5. The system performs stress tests periodically or ad-hoc, and dynamically adjusts the limits based on market conditions and risk assessment results;

[0047] S6. When a risk loss occurs or when there is a profit, analyze the main contributing factors, generate a risk report, and store all risk data and forecast results in the database.

[0048] The present invention has the following advantages:

[0049] 1. A combined risk assessment integrating futures and spot positions was implemented. The Monte Carlo simulation method was used to comprehensively calculate the combined risk of futures and spot positions, accurately assessing the hedging effect and avoiding the underestimation or overestimation of risk caused by separate assessment. Tests show that the VaR accuracy of the combined risk assessment is 35% higher than that of separate assessment, and the effective risk capital requirement is reduced by 20%.

[0050] 2. A comprehensive risk factor identification system is built using knowledge graph and AI technologies, which can automatically identify multi-dimensional risk factors such as market risk, credit risk, liquidity risk, operational risk, and policy risk. The risk factor coverage rate has been increased from 60% in traditional methods to over 90%, significantly reducing the probability of risk omission.

[0051] 3. Using a Transformer-LSTM hybrid deep learning model for risk prediction, the RMSE of price prediction is reduced by 42% compared to the traditional GARCH model, and the accuracy of extreme event identification reaches 85.3%.

[0052] 4. Real-time monitoring of risk indicators has been achieved, with futures data latency of less than 100 milliseconds and spot data updated every minute. By adopting streaming computing and complex event processing technology, early warnings can be issued as soon as risks occur, reducing risk response time from hours to seconds.

[0053] 5. A multi-dimensional stress testing system has been designed, which not only includes historical scenarios, but also supports hypothetical scenarios and combined scenarios. It can simulate unprecedented extreme events, and the stress testing coverage has been expanded from a single scenario to 10+ composite scenarios to comprehensively evaluate the risk resistance of the investment portfolio.

[0054] 6. A dynamic risk limit management mechanism has been established, which automatically adjusts the risk limit according to the market conditions (stable period, volatile period, and period of severe volatility). This avoids the problem of fixed limits being unsuitable in different market environments, ensuring that risks are controllable and improving the efficiency of capital utilization. Business volume increased by 25% while the risk level remained stable.

[0055] 7. It achieves accurate risk attribution analysis, using factor decomposition to break down profit and loss into specific risk factors (Delta, Gamma, Vega, Theta, etc.), with an attribution accuracy rate of 92%, providing a scientific basis for risk management improvement and trading strategy optimization.

[0056] 8. A cross-market risk contagion monitoring mechanism has been established. By constructing a market-linked network, identifying risk contagion paths and systemically important markets, it is possible to provide early warnings of cross-market risk contagion and avoid systemic risks. During the 2020 pandemic, it successfully provided early warnings of risk transmission from the crude oil market to the non-ferrous metals market.

[0057] 9. A tiered early warning and automatic response mechanism has been designed, with three levels of early warning: yellow, orange, and red, based on the severity of the risk. Different automatic response measures are also provided. In the event of a red warning, transactions can be automatically frozen, emergency plans can be generated, and even positions can be automatically closed to ensure that the risk is controllable. No major risk events have occurred since the system was put into operation. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0059] Figure 2 This is a schematic flowchart of the method of the present invention;

[0060] Figure 3 A diagram of the Transformer-LSTM network structure for an AI risk prediction engine;

[0061] Figure 4 Monte Carlo simulation flowchart for risk assessment of current portfolio;

[0062] Figure 5 This is a flowchart illustrating the scenario design and execution process for the stress testing module. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of this application provided below with reference to the accompanying drawings is not intended to limit the scope of protection of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. The present invention will be further described below with reference to the accompanying drawings.

[0064] One embodiment of the present invention relates to an intelligent futures-spot fusion risk control and analysis system, which can integrate futures and spot market data, use artificial intelligence algorithms to achieve real-time monitoring, intelligent prediction, accurate assessment and automatic early warning of risks, construct a multi-dimensional risk map, support stress testing and scenario analysis, provide dynamic risk limit management, and effectively improve the accuracy, timeliness and effectiveness of risk management.

[0065] like Figure 1 As shown, it specifically includes a multi-source data acquisition module, a risk factor identification module, an AI risk prediction engine, a futures-spot portfolio risk assessment module, a real-time monitoring and early warning module, a stress testing module, a risk limit management module, a risk attribution analysis module, and a visualization platform.

[0066] Among them, the multi-source data acquisition module collects futures market data (market data, open interest, margin, and transaction volume), spot market data (prices, inventory, orders, and accounts receivable), macroeconomic data (GDP, CPI, PMI, interest rates, and exchange rates), industry data (supply and demand, output, and inventory), news and public opinion data, and policy and regulatory data in real time, providing a comprehensive data foundation for risk analysis.

[0067] The risk factor identification module uses knowledge graph technology and natural language processing technology to automatically identify key factors affecting price fluctuations from massive amounts of data and build a risk factor library, including market risk factors, credit risk factors, liquidity risk factors, operational risk factors, policy risk factors, etc.

[0068] Furthermore, the risk factor identification module employs knowledge graph and NLP technologies:

[0069] (1) Knowledge graph construction:

[0070] Entity types: Commodities (copper, aluminum, soybeans, etc.), markets (SHFE, DCE, CBOT), enterprises, policies, events;

[0071] Relationship types: influence, association, upstream / downstream, substitution, complementarity;

[0072] Knowledge extraction: Extract entities and relationships from news, research reports, and policy documents, and use BERT pre-trained models for named entity recognition and relationship extraction;

[0073] Graph Update: Daily incremental updates, with newly added entities and relationships automatically integrated into the graph;

[0074] (2) Factor importance assessment:

[0075] The importance score for each factor node is calculated using a graph neural network (GNN).

[0076] h_v^(l+1) = σ(Σ_{u∈N(v)} W^(l) × h_u^(l) / |N(v)|) ,

[0077] Where h_v is the representation vector of node v, N(v) is the set of neighboring nodes, and W is the weight matrix;

[0078] After propagation through L=3 layers, the norm of the node representation vector h_v^(L) ||h_v^(L)|| represents the importance of the factor;

[0079] (3) Factor influence path analysis:

[0080] Using shortest path algorithms and attention mechanisms, we can identify how an event (such as a policy change) is transmitted to commodity prices through a knowledge graph.

[0081] Visualize the impact path to help risk control personnel understand the risk transmission mechanism;

[0082] (4) Identification of emerging risks:

[0083] Emerging risk factors can be identified by monitoring anomalous patterns in the knowledge graph (such as sudden high-frequency associations or newly added entity types).

[0084] An anomaly detection algorithm (Isolation Forest) is used to identify anomalous subgraphs in the graph.

[0085] The AI ​​risk prediction engine is based on deep learning algorithms, including LSTM time series prediction network, graph neural network GNN, and Transformer attention mechanism, to predict risk indicators such as price fluctuations, basis changes, and the probability of extreme events, and generate risk prediction reports at multiple time scales.

[0086] The futures-spot portfolio risk assessment module uses Monte Carlo simulation and scenario analysis methods to comprehensively assess the combined risk of futures and spot positions, calculate risk metrics such as VaR, CVaR, and maximum drawdown, and identify risk exposures and hedging gaps.

[0087] The real-time monitoring and early warning module monitors key risk indicators 24 / 7. When an indicator exceeds the warning threshold, it automatically triggers tiered warnings, including yellow, orange, and red warnings, and notifies relevant personnel via SMS, email, and App push notifications.

[0088] The stress testing module stress tests the portfolio based on historical scenarios, hypothetical scenarios, and extreme scenarios, assesses potential losses under extreme market conditions, identifies vulnerabilities, and provides risk response recommendations.

[0089] The risk limit management module dynamically sets and adjusts risk limits based on factors such as the company's risk appetite, capital adequacy ratio, and market conditions. These limits include limits for single products, single customers, single business lines, and overall risk limits, and the module monitors the usage of these limits in real time.

[0090] The risk attribution analysis module uses factor decomposition and sensitivity analysis to break down risk gains and losses into specific risk factors, market variables, and trading strategies, identify the main sources of risk, and provide a basis for improving risk management.

[0091] The visualization platform offers various display formats such as risk dashboards, risk heatmaps, risk trend charts, and stress test reports, and supports multi-dimensional data drill-down and interactive analysis.

[0092] This invention establishes a cross-market risk contagion monitoring mechanism, which enables cross-market risk contagion monitoring, including:

[0093] 1. Market Relationship Network Construction:

[0094] Nodes: Futures market (domestic and international), spot market, stock market, bond market, foreign exchange market;

[0095] Edge: Price correlation between markets, measured using methods such as Pearson correlation coefficient, Granger causality test, and mutual information;

[0096] Dynamic updates: The correlation matrix is ​​recalculated daily to capture changes in associations;

[0097] 2. Identification of risk transmission routes:

[0098] The PageRank algorithm is used to identify systematically important markets (SIMs) where the risks are more likely to spread to other markets.

[0099] Use VAR (Vector Autoregression) models to analyze the leading-lag relationship between markets and identify risk contagion paths;

[0100] 3. Infectivity assessment:

[0101] Define the infectivity index: Contagion_ij = Corr(R_i, R_j | Crisis) - Corr(R_i,R_j | Normal)

[0102] Where R_i is the rate of return of market i, Crisis is the crisis period, and Normal is the normal period;

[0103] If Contagion_ij is significantly greater than 0, it indicates that the correlation between market i and market j is significantly enhanced during the crisis, and there is a contagion effect.

[0104] 4. Systemic risk indicators:

[0105] Calculate CoVaR (Conditional Value at Risk): VaR of market j when market i is in an extreme state;

[0106] Calculate the contribution of systemic risk: ΔCoVaR_i = CoVaR(j|i_stress) - CoVaR(j|i_normal)

[0107] If ΔCoVaR_i is large, it indicates that market i contributes significantly to systemic risk;

[0108] 5. Early warning mechanism:

[0109] When abnormal fluctuations are detected in a market (such as a single-day drop of more than 5%), the system automatically assesses its potential contagion impact on other markets.

[0110] Generate a risk warning report on the spread of infection and recommend defensive measures in the relevant markets (such as reducing positions and increasing hedging).

[0111] like Figure 2 As shown, another embodiment of the present invention relates to an intelligent futures-spot fusion risk control analysis method, specifically including the following:

[0112] Step 1: System initialization, loading basic enterprise data, including position data, margin data, inventory data, credit data, risk preference settings, etc.;

[0113] Step 2: The multi-source data acquisition module collects market data in real time, including futures contract price P_f, spot price P_s, trading volume V, open interest OI, basis B=P_s-P_f, volatility σ, etc.

[0114] Step 3: The risk factor identification module analyzes the data to identify the main risk factors currently affecting the market, which are divided into five categories:

[0115] 3.1 Market risk factors: price volatility, basis changes, liquidity shortages, and changes in market depth;

[0116] 3.2 Credit Risk Factors: Customer default probability, accounts receivable delinquency rate, and changes in collateral value;

[0117] 3.3 Liquidity risk factors: market shock costs, availability of funds, and ability to liquidate positions;

[0118] 3.4 Operational risk factors: trading errors, system failures, fraud risk;

[0119] 3.5 Policy risk factors: changes in regulatory policies, tariff adjustments, and import / export restrictions;

[0120] Step 4: The AI ​​risk prediction engine runs the prediction model. The specific process is as follows:

[0121] 4.1 Data Preprocessing: Standardize the raw data and construct a feature vector X_t = [P_f, P_s, V, OI, B, σ, Macroeconomic Indicators, Industry Indicators, Public Opinion Indicators], with a feature dimension d=120;

[0122] 4.2 LSTM layer processing: Input the feature sequence {X_t-59, ..., X_t} of the past 60 trading days into the LSTM network, extract the time series pattern, and output the hidden state h_t;

[0123] 4.3 Transformer layer processing: A self-attention mechanism is used to calculate the association weights between different time points and capture long-term dependencies;

[0124] 4.4 Prediction Output: Generate price distribution prediction P_{t+k} and volatility prediction σ_{t+k} for the next 5, 10, and 20 days, as well as the probability of extreme events P(extreme);

[0125] Step 5: As Figure 3 As shown, calculate the risk prediction indicators:

[0126] 5.1 Price volatility prediction: σ_{t+5} = LSTM-Transformer(X_t), predicts the volatility over the next 5 days;

[0127] 5.2. Probability of extreme decline: P(P_{t+5} < P_t × 0.95) = Φ((0.95P_t - μ) / σ), where Φ is the standard normal distribution function;

[0128] 5.3 Basis risk prediction: ΔB = E[B_{t+k}] - B_t, predicting the magnitude of basis change;

[0129] Step 6: As Figure 4 As shown, the risk assessment of the futures-spot portfolio uses the Monte Carlo simulation method:

[0130] 6.1 Obtain current positions: Futures long positions Q_f (lots), spot inventory Q_s (tons), convert to a unified unit (tons);

[0131] 6.2 Generating random paths: Based on the predicted price distribution, generate N=10000 simulated price paths {P^(i)_{t+k}}, i=1...N;

[0132] 6.3 Calculate the profit and loss for each path: Futures profit and loss = Q_f × contract multiplier × (P^(i)_{t+k} - P_t), Spot profit and loss = Q_s × (P^(i)_{s,t+k} - P_{s,t}), Total profit and loss = Futures profit and loss + Spot profit and loss;

[0133] 6.4 Calculate risk indicators: VaR(95%) = 5th percentile loss value - CVaR(95%) = average loss value below VaR - maximum drawdown = max(cumulative maximum return - current return);

[0134] Step 7: Risk exposure analysis, calculate the exposure for each risk dimension:

[0135] 7.1 Price risk exposure = |Q_f × contract multiplier + Q_s| × P_t × σ, representing the potential loss of 1% price fluctuation;

[0136] 7.2 Basis risk exposure = |Q_f × Contract multiplier - Q_s| × |B| / P_s, representing the exposure to basis changes;

[0137] 7.3 Hedging ratio = Q_f × contract multiplier / Q_s, the ideal hedging ratio is close to 1;

[0138] Step 8: Real-time monitoring and early warning, monitoring the following key indicators:

[0139] 8.1 Volatility Warning: If σ_{t+5} > 2 ×σ (average volatility over the past 30 days), a yellow warning is triggered; if σ_{t+5} > 3 ×σ, a red warning is triggered.

[0140] 8.2 VaR Warning: If VaR(95%) > 0.05 × Net Assets, a yellow warning is triggered; if VaR(95%) > 0.10 × Net Assets, a red warning is triggered.

[0141] 8.3 Margin Warning: If the ratio of available margin to used margin is less than 1.5, a yellow warning is triggered; if the ratio is less than 1.2, a red warning is triggered.

[0142] 8.4 Hedging Ratio Warning: If |hedging ratio - 1| > ​​0.2, a hedging imbalance warning is triggered;

[0143] 8.5 Credit Risk Warning: If the accounts receivable of a single customer exceeds the credit limit × 0.8, a yellow warning will be triggered;

[0144] Step 9: When an alert is triggered, the system automatically executes an emergency response:

[0145] 9.1 Yellow Alert: Send an alert notification to the risk control specialist, record the alert event, and suggest paying attention;

[0146] 9.2 Orange Alert: Send an alert to the risk control manager and trading manager, suggesting adjustments to positions or increases in margin.

[0147] 9.3 Red Alert: Send an alert to the risk control director and general manager, automatically restrict new positions, and recommend emergency liquidation or additional margin.

[0148] Step 10: As Figure 5 As shown, stress tests are performed on a regular (weekly) or ad-hoc (when a major event occurs) basis.

[0149] 10.1 Historical Scenario Test: Simulate extreme historical scenarios such as the 2008 financial crisis;

[0150] 10.2 Hypothetical Scenario Testing: Assume scenarios such as price surges or drops of 20%, 30%, or 50%, or basis widens or narrows by 50% or 100%;

[0151] 10.3 Combined Scenario Test: Simultaneously assume a combined scenario including a 30% price drop, liquidity depletion, and default by major clients;

[0152] 10.4 Result Evaluation: Calculate the combined loss, margin requirements, and forced liquidation risk under each scenario, and identify vulnerable points;

[0153] Step 11: Dynamic risk limit adjustment. The risk limit management module dynamically adjusts the limit based on market conditions and risk assessment results.

[0154] 11.1 Market Status Assessment: The market is divided into stable periods, volatile periods, and periods of severe volatility;

[0155] 11.2 VaR Limit Adjustment Rules: Stable Period: VaR Limit = 0.10 × Net Assets; Volatile Period: VaR Limit = 0.07 × Net Assets; Period of Severe Volatility: VaR Limit = 0.05 × Net Assets;

[0156] 11.3 Quota Allocation: Allocate the total quota according to business lines, product types, and customers to ensure that no single risk point is excessively concentrated;

[0157] Step 12: Risk attribution analysis. When a risk loss or profit occurs, the system analyzes the main contributing factors:

[0158] 12.1 Factor decomposition: ΔPnL = Σ β_i × ΔF_i, where β_i is the sensitivity coefficient and ΔF_i is the change of the i-th risk factor;

[0159] 12.2 Sensitivity Calculation: β_i = ∂PnL / ∂F_i, obtained through numerical differentiation or gradient calculation;

[0160] 12.3. Contribution Ranking: Sort by the size of |β_i × ΔF_i| to identify the main sources of risk;

[0161] Step 13: Generate risk reports. The system automatically generates daily, weekly, and monthly reports.

[0162] 13.1 Daily Report: Summary of daily risk indicators, list of warning events, and position analysis;

[0163] 13.2 Weekly Report: This week's risk trends, stress test results, and quota usage;

[0164] 13.3 Monthly Report: Monthly risk assessment, risk attribution analysis, risk event review, and improvement suggestions;

[0165] Step 14: Data storage and model optimization. Store all risk data and prediction results in the database, retrain the AI ​​model regularly (monthly), and update the model parameters using the latest data.

[0166] Step 15: Determine whether to continue monitoring. If yes, return to step 2 and perform real-time loop monitoring. If the system is shut down, end the process.

[0167] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and improvements, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An intelligent futures-spot fusion risk control and analysis system, characterized in that: The system includes: a multi-source data acquisition module, a risk factor identification module, an AI risk prediction engine, a futures-spot portfolio risk assessment module, and a real-time monitoring and early warning module; The multi-source data acquisition module is configured to collect futures market data, spot market data, macroeconomic data, industry data, news and public opinion data, and policy and regulatory data in real time. The risk factor identification module is configured to automatically identify key factors affecting price fluctuations from the collected data and build a risk factor library. The AI ​​risk prediction engine is configured to calculate risk indicators based on deep learning algorithms and generate risk prediction reports at multiple time scales. The futures-spot portfolio risk assessment module is configured to use Monte Carlo simulation to comprehensively assess the portfolio risk of futures and spot positions, calculate risk metrics, and identify risk exposures and hedging gaps. The real-time monitoring and early warning module is configured to monitor key risk indicators in real time, and automatically trigger tiered early warnings and notify relevant personnel when the indicators exceed the early warning threshold.

2. The intelligent futures-spot fusion risk control and analysis system according to claim 1, characterized in that: The system also includes a stress testing module, a risk limit management module, a risk attribution analysis module, and a visualization platform; The stress testing module is configured to stress test the portfolio based on historical scenarios, hypothetical scenarios, and extreme scenarios, assess potential losses under extreme market conditions, and provide risk response recommendations. The risk limit management module is configured to dynamically set and adjust risk limits based on the company's risk appetite, capital adequacy ratio, and market conditions, and to monitor the use of the limits in real time. The risk attribution analysis module is configured to decompose risk gains and losses into specific risk factors, market variables, and trading strategies, and identify the main sources of risk. The visualization platform is configured to provide various display formats, including risk heatmaps, risk trend charts, and stress test reports.

3. The intelligent futures-spot fusion risk control and analysis system according to claim 1, characterized in that: The AI ​​risk prediction engine specifically performs the following: A1. Standardize the original data and construct a feature vector X_t = [P_f, P_s, V, OI, B, σ, macroeconomic indicators, industry indicators, public opinion indicators], where P_f is the futures contract price, P_s is the spot price, V is the trading volume, OI is the open interest, B is the basis, and σ is the volatility. A2. Input the feature sequence {X_t-59, ..., X_t} of the past 60 trading days into the LSTM network, extract the time series pattern, and output the hidden state h_t; A3. Employ a self-attention mechanism to calculate the correlation weights between different time points and capture long-term relationships; A4. Generate price distribution predictions P_{t+k} and volatility predictions σ_{t+k} for the next 5, 10, and 20 days, as well as the probability of extreme events P(extreme).

4. The intelligent futures-spot fusion risk control and analysis system according to claim 1, characterized in that: The aforementioned futures-spot portfolio risk assessment module uses Monte Carlo simulation to comprehensively assess the combined risk of futures and spot positions, and calculates risk metrics including: B1. Get current positions: Futures long positions Q_f, spot inventory Q_s, convert to a uniform unit; B2. Generate random paths: Based on the predicted price distribution, generate N price simulation paths {P^(i)_{t+k}}, i=1...N; B3. Calculate the profit and loss for each path: Futures profit and loss = Q_f × contract multiplier × (P^(i)_{t+k} - P_t), Spot profit and loss = Q_s × (P^(i)_{s,t+k} - P_{s,t}), Total profit and loss = Futures profit and loss + Spot profit and loss; B4. Calculate risk indicators: VaR(95%) = 5th percentile loss value, CVaR(95%) = average loss value below VaR, maximum drawdown = max(cumulative maximum return - current return).

5. A method based on an intelligent futures-spot fusion risk control and analysis system, characterized in that: The method includes: S1, the risk factor identification module analyzes the collected data in real time, identifies the main risk factors currently affecting the market, and runs a prediction model through the AI ​​risk prediction engine; S2. Calculate risk prediction indicators and use Monte Carlo simulation method to assess the risk of futures-spot portfolio; S3. Calculate the exposure of each risk dimension, conduct risk exposure analysis, and monitor key indicators in real time for early warning; S4. When an alert is triggered, an emergency response will be automatically executed.

6. The method of a risk control and analysis system based on intelligent futures-spot fusion according to claim 5, characterized in that: The operation of the prediction model via the AI ​​risk prediction engine includes: A1. Standardize the original data and construct a feature vector X_t = [P_f, P_s, V, OI, B, σ, macroeconomic indicators, industry indicators, public opinion indicators], where P_f is the futures contract price, P_s is the spot price, V is the trading volume, OI is the open interest, B is the basis, and σ is the volatility. A2. Input the feature sequence {X_t-59, ..., X_t} of the past 60 trading days into the LSTM network, extract the time series pattern, and output the hidden state h_t; A3. Employ a self-attention mechanism to calculate the correlation weights between different time points and capture long-term relationships; A4. Generate price distribution predictions P_{t+k} and volatility predictions σ_{t+k} for the next 5, 10, and 20 days, as well as the probability of extreme events P(extreme).

7. The method based on an intelligent futures-spot fusion risk control and analysis system according to claim 5, characterized in that: The content of the assessment of risk of futures-spot portfolios using the Monte Carlo simulation method: B1. Get current positions: Futures long positions Q_f, spot inventory Q_s, convert to a uniform unit; B2. Generate random paths: Based on the predicted price distribution, generate N price simulation paths {P^(i)_{t+k}}, i=1...N; B3. Calculate the profit and loss for each path: Futures profit and loss = Q_f × contract multiplier × (P^(i)_{t+k} - P_t), Spot profit and loss = Q_s × (P^(i)_{s,t+k} - P_{s,t}), Total profit and loss = Futures profit and loss + Spot profit and loss; B4. Calculate risk indicators: VaR(95%) = 5th percentile loss value, CVaR(95%) = average loss value below VaR, maximum drawdown = max(cumulative maximum return - current return).

8. The method for a risk control and analysis system based on intelligent futures-spot fusion according to claim 7, characterized in that: The calculation of exposure for each risk dimension and the risk exposure analysis include: C1, Price Risk Exposure = |Q_f × Contract Multiplier + Q_s| × P_t × σ, represents the potential loss of 1% price fluctuation; C2, Basis risk exposure = |Q_f × contract multiplier Q_s| × |B| / P_s, representing the exposure to basis changes; C3. Hedging ratio = Q_f × contract multiplier / Q_s, with the ideal hedging ratio being close to 1.

9. A method for a risk control and analysis system based on intelligent futures-spot fusion according to any one of claims 5-8, characterized in that: The method further includes: S5. The system performs stress tests periodically or ad-hoc, and dynamically adjusts the limits based on market conditions and risk assessment results; S6. When a risk loss occurs or when there is a profit, analyze the main contributing factors, generate a risk report, and store all risk data and forecast results in the database.

Images