2022年美赛特等奖论文-2022-2022年C题获奖论文合集.pdf

In the financial market, higher profits and lower risks are the goals that people seek, but they are always

contradictory. Therefore, researchers in the financial field are looking for more efficient and versatile

not very high, just like a conservative investment strategy or a way of preserving the value of an asset.

In this part, we analyze the property of isometric grid strategy and proportional grid strategy, then

compare their profits. We conclude that the large-width isometric grid is more suitable for bitcoin and

gold trading.

Next, since grid trading is too conservative and stable to satisfy our requirement for high profit, we have

to make some improvements to it. From a long-term perspective, the moving average (MA), which is

frequently used in stocks or futures trading, is introduced to make the model follow the overall market

trend. To further improve our model, we notice the classic model, auto-regressive integrated moving

average(ARIMA). ARIMA model can give a good prediction for stationary time series. Note that real

market conditions do not guarantee that price changes will be stationary, but we can adjust the weight

of risk assessment, these simple strategies have huge risks and randomness, which is inapplicable.

Because our model has very good generalization ability, we consider our model to be very good when

considering the factors of risk-return.

Last but not least, we test the properties of our models from the perspective of sensitivity and robust-

ness. Our model maintains high stability under the influence of changing transaction rates slightly and

noisy price data. In the case of noisy data, the grid strategy takes advantage of the shock market and

arbitrage more. In the event of a slight change in transaction rate, the model automatically switched to

a larger grid and reduces transaction frequency to adapt to different environments.

Keywords: grid strategy, ARIMA, time series analysis, quantitative trading, statistical test

2 Assumptions 3

3 Notations 4

4 Vanilla Grid Strategy 5

5 Adaptive Periodic Grid Model 9

7 Model Evaluation 17

8 Strengths and Weaknesses 19

9 Conclusion 20

10 Memorandum 21

11 Reference 23

earliest developed and largest cryptocurrency, is called "digital gold", and blockchain technology is

also popular due to its decentralization property and no need for supervision. In face of these assets,

many people will try to invest personally, but their investment results are mixed. To ensure lower risk

and higher profit, quantitative investment strategies have developed rapidly in recent years. Quantitative

investment mainly depends on the analysis of historical data, the prediction of the market development

trend, potential profit and risk in order to make a suitable decision. In the establishment of quantitative

investment strategies, we usually need to find an appropriate model to replace manual prediction and

decision-making.

1.2 Restatement of the problem

Considering the background of the question and the limitations, we decide to focus on the following

1.3 Literature Review

In the field of quantitative trading, financial time series modeling mainly focus on the field of

ARIMA(auto-regressive integrated moving average) model and some modifications to this model.

The popularity of the ARIMA model is due to its statistical properties as well as the well-known

Box–Jenkins methodology[2]. The work of Minyong Kim in 2015[4] showed that ARIMA provided

traditional grid strategies are the Forex Grid strategy[7] and the Gann Grid strategy[10]

1.4 Our work && model overview

We proposed an APGM(Adaptive Periodic Grid Model) to decide when and how to change our posi-

tions in the Gold and Bitcoin markets. This model uses ARIMA predictions and AM to shift the grid.

This approach enables grid model to make decisions when market price change immediately. We also

design a backtesting process to adaptively adjust the parameters of our APGM. At the end, we write a

memorandum to communicate your strategy, model, and results to the trader.

The following is a flow chart of our model framework.

2 Assumptions

Since we can only use the data provided, we make the following assumption:

3 Notations