SAC | 24.8449 | 1.1234 | 11.9776 |  | 38.3318 | 1.0733 | 13.8432 |  | 23.8034 | 0.7506 | 13.0667 |  | 22.0218 | 0.8177 | 12.9214 |  | 42.2209 | 0.8727 | 26.1947 |  | 17.8439 | 0.7635 | 10.0587
PPO | 13.2619 | 0.6096 | 14.7809 |  | 21.1745 | 0.6965 | 13.8432 |  | 38.2907 | 1.2982 | 8.4536 |  | 11.3219 | 0.4831 | 17.5054 |  | 33.6444 | 0.7767 | 28.3527 |  | 34.7469 | 1.3096 | 11.1171
LLM-based | FinGPT | -5.4632 | -0.1731 | 16.2268 |  | 42.9331 | 1.1026 | 18.9359 |  | 12.2770 | 0.4444 | 13.0013 |  | 25.1012 | 0.9667 | 9.8426 |  | 38.4338 | 0.7504 | 31.4740 |  | 21.5746 | 0.6801 | 25.5620
FinMem | 23.7809 | 1.1073 | 10.3872 |  | 40.07 | 1.034 | 18.5279 |  | 31.2716 | 1.1073 | 8.9706 |  | 40.5757 | 1.4989 | 7.4838 |  | 50.0353 | 0.9233 | 25.7714 |  | 44.717 | 1.2738 | 13.587
FinAgent (Our) | w/o-MLH | 18.5186 | 0.9882 | 11.8842 |  | 62.3106 | 1.6379 | 11.3999 |  | 37.3308 | 1.2657 | 12.4859 |  | 18.2970 | 0.8945 | 9.6792 |  | 39.0087 | 0.8971 | 22.5376 |  | 16.2106 | 0.6329 | 15.9325
w/o-LHT | 12.6872 | 0.5849 | 14.7806 |  | 43.2195 | 1.1057 | 14.6786 |  | 17.4140 | 0.5763 | 13.0060 |  | 18.4571 | 0.7108 | 14.2551 |  | 39.2704 | 0.7662 | 30.1507 |  | 25.9708 | 0.7718 | 24.4314
w/o-HT | 21.3044 | 0.9777 | 13.6424 |  | 47.3916 | 1.2032 | 15.5038 |  | 29.3235 | 1.0177 | 8.9706 |  | 39.9073 | 1.4777 | 7.4838 |  | 57.1638 | 1.0201 | 25.7714 |  | 52.3265 | 1.3351 | 13.5870
w/o-T | 33.7509 | 1.5205 | 8.1783 |  | 63.8116 | 1.7009 | 9.7325 |  | 52.1066 | 1.8228 | 8.3106 |  | 42.5213 | 1.4909 | 7.6998 |  | 89.2532 | 1.4573 | 27.6213 |  | 54.8040 | 1.4030 | 11.7427
FinAgent | 31.8972 | 1.4326 | 2.9424 |  | 65.0998 | 1.6096 | 4.4841 |  | 56.1542 | 1.7786 | 6.0365 |  | 44.7359 | 1.7884 | 7.4209 |  | 92.2677 | 2.0088 | 6.4543 |  | 43.0822 | 1.1773 | 3.3078
Improvement(%) | 35.8464 | 35.3481 | 7.9217 |  | 50.4556 | 45.3636 | - |  | 46.6523 | - | - |  | 10.2529 | 19.3142 | 10.4131 |  | 84.4052 | 117.5674 | 14.2116 |  | 22.5574 | 7.1319 | -
Categories | Models | AAPL |  | AMZN |  | GOOGL |  | MSFT |  | TSLA |  | ETHUSD
SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$ |  | SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$ |  | SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$ |  | SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$ |  | SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$ |  | SOR$\uparrow$ | CR$\uparrow$ | VOL$\downarrow$
Market | B&H | 16.5846 | 0.9589 | 0.0114 |  | 35.1804 | 2.4319 | 0.0188 |  | 18.5186 | 1.9025 | 0.0167 |  | 26.5133 | 1.8135 | 0.0135 |  | 23.3319 | 1.3856 | 0.0301 |  | 23.2235 | 1.3831 | 0.0222
Rule-based | MACD | 13.7755 | 1.1877 | 0.0082 |  | 19.2452 | 1.9176 | 0.0103 |  | -20.9170 | -0.8883 | 0.0097 |  | 18.7485 | 1.8966 | 0.0099 |  | -0.4067 | -0.0544 | 0.0182 |  | 10.8951 | 0.5433 | 0.0166
KDJ&RSI | 3.3994 | 0.2578 | 0.0084 |  | 16.6915 | 1.2471 | 0.0160 |  | 36.6655 | 11.4076 | 0.0052 |  | 19.5346 | 2.4169 | 0.0085 |  | 3.3617 | 0.2737 | 0.0192 |  | 7.7786 | 0.6106 | 0.0120
ZMR | -2.9977 | -0.3700 | 0.0072 |  | 9.5853 | 2.4473 | 0.0110 |  | 35.1125 | 5.7782 | 0.0103 |  | 12.9275 | 1.6385 | 0.0069 |  | -1.8214 | -0.1642 | 0.0182 |  | 21.4433 | 2.1715 | 0.0137
RL-based | DQN | 10.3705 | 0.6266 | 0.0111 |  | 29.7698 | 5.1156 | 0.0111 |  | 37.7389 | 4.6190 | 0.0114 |  | 32.8642 | 2.8493 | 0.0122 |  | 11.0394 | 0.7399 | 0.0225 |  | 24.6279 | 3.0545 | 0.0143
SAC | 33.5676 | 2.0552 | 0.0105 |  | 32.4320 | 2.7660 | 0.0112 |  | 19.3937 | 1.9708 | 0.0165 |  | 26.0662 | 1.7829 | 0.0135 |  | 26.2869 | 1.4410 | 0.0251 |  | 13.3918 | 1.9199 | 0.0146
PPO | 16.7660 | 0.9747 | 0.0113 |  | 20.3167 | 1.6735 | 0.0159 |  | 42.7201 | 4.3691 | 0.0136 |  | 14.7808 | 0.7484 | 0.0130 |  | 19.1547 | 1.3203 | 0.0231 |  | 27.2416 | 2.9836 | 0.0147
LLM-based | FinGPT | -4.6731 | -0.2460 | 0.0111 |  | 34.8082 | 2.2545 | 0.0186 |  | 11.6596 | 1.1842 | 0.0167 |  | 30.1935 | 2.5867 | 0.0127 |  | 23.1813 | 1.4402 | 0.0291 |  | 18.5817 | 1.0131 | 0.0221
FinMem | 29.8819 | 2.2731 | 0.0102 |  | 33.0779 | 2.1843 | 0.0188 |  | 34.7826 | 3.4572 | 0.0134 |  | 47.1061 | 5.1266 | 0.0123 |  | 25.8819 | 2.0887 | 0.028 |  | 34.1492 | 3.1349 | 0.0194
FinAgent (Our) | w/o-MLH | 20.8183 | 1.5678 | 0.0090 |  | 53.4328 | 4.9724 | 0.0166 |  | 24.9489 | 2.9016 | 0.0137 |  | 22.0401 | 1.9273 | 0.0100 |  | 20.3649 | 1.8355 | 0.0221 |  | 14.0758 | 1.1917 | 0.0174
w/o-LHT | 16.1500 | 0.9396 | 0.0114 |  | 35.1674 | 2.9256 | 0.0186 |  | 15.1339 | 1.5520 | 0.0168 |  | 22.4806 | 1.3883 | 0.0133 |  | 22.2082 | 1.5162 | 0.0286 |  | 21.0778 | 1.2131 | 0.0222
w/o-HT | 29.5194 | 1.5760 | 0.0105 |  | 40.4896 | 2.9758 | 0.0184 |  | 33.6191 | 3.2906 | 0.0139 |  | 46.4396 | 5.0540 | 0.0123 |  | 28.3015 | 2.2956 | 0.0278 |  | 37.1720 | 3.6027 | 0.0212
w/o-T | 46.6145 | 3.9301 | 0.0101 |  | 50.1096 | 5.9255 | 0.0162 |  | 62.2508 | 5.7201 | 0.0125 |  | 40.3937 | 5.2137 | 0.0129 |  | 41.5642 | 2.9306 | 0.0266 |  | 37.4619 | 4.2958 | 0.0208
FinAgent | 44.2812 | 10.4032 | 0.0102 |  | 52.5602 | 13.198 | 0.0176 |  | 62.2992 | 8.4532 | 0.0138 |  | 49.6249 | 5.5732 | 0.0111 |  | 45.4139 | 12.143 | 0.0187 |  | 31.0159 | 12.7171 | 0.0207
Improvement(%) | 38.8675 | 72.8960 | - |  | 51.8823 | 15.8320 | - |  | 45.3630 | - | - |  | 5.3471 | 44.7529 | - |  | 72.7625 | 209.0104 | - |  | 9.7007 | 37.0315 | -

##  Appendix I Details of FinAgent analysis

We provide a detailed analysis of FinAgent’s performance in this section and
will provide some decision-making case studies.

###  I.1. Case Study I

Figure 7. Performance analysis of FinAgent for AAPL.

##### Example 1

⬇

1Action: BUY

2Reasoning: The decision to BUY is based on several factors: Firstly, despite
the presence of negative sentiment concerning the NSA spy allegations and
customer withdrawals, the market intelligence is largely positive due to the
expected impact of
Apple’s␣new␣AR/VR␣product␣line␣which␣influences␣a␣positive␣medium-
term␣outlook.Secondly,␣the␣consistent␣price␣gain␣across␣all␣time␣horizons␣-␣short,␣medium,␣and␣long-
term␣-␣supports␣a␣bullish␣future␣trend,␣aligning␣with␣the␣momentum␣principle␣of␣financial␣market␣analysis.␣Past␣reflections␣on␣trading␣decisions␣indicate␣that␣not␣buying␣was␣a␣missed␣opportunity␣for␣profit,␣suggesting␣that␣a␣BUY␣decision␣now␣would␣be␣timely␣to␣capture␣potential␣gains.␣Although␣professional␣investment␣guidances␣show␣mixed␣sentiment,␣the␣historical␣performance␣of␣the␣company,␣continued␣innovation,␣and␣the␣influential␣design␣culture␣suggest␣a␣more␣bullish␣long-
term␣perspective.␣The␣trading␣strategies,␣while␣currently␣indicating␣HOLD,␣do␣not␣provide␣a␣strong␣counterargument␣against␣buying,␣as␣they␣suggest␣no␣clear␣downward␣trend␣and␣no␣sell␣signals␣have␣been␣generated.␣Lastly,␣the␣financial␣ability␣to␣make␣a␣purchase␣(sufficient␣CASH␣with␣no␣existing␣POSITION)␣supports␣the␣initiation␣of␣a␣new␣position␣in␣AAPL␣stock.␣Considering␣these␣combined␣factors,␣particularly␣the␣medium-
term␣positive␣market␣sentiment␣and␣upward␣price␣trend,␣a␣BUY␣decision␣appears␣to␣be␣the␣appropriate␣action␣to␣potentially␣maximize␣returns.’

##### Example 2

⬇

1Action: SELL

2Reasoning: The decision to SELL is based on several analyses: 1. Market
intelligence indicates mixed sentiment but with a discernible lean towards
negativity in the medium to long term, suggesting potential for price
depreciation in the future. While there are short-term bullish sentiments
expressed in the intelligence, these are considered less influential in the
decision-making process due to the prevailing medium and long-term negative
outlooks. 2. The price movement analysis, showing recent declines and a lack
of consistent positive trend in the medium to long-term, corroborates the
intelligence sentiment and advocates for a SELL to mitigate potential future
losses. 3. Reflections on past trading decisions have indicated that selling
in the early stages of a bearish trend can prevent significant losses,
emphasizing that future trading decisions should consider both the bearish
sentiment and momentum. 4.In the absence of professional investment guidance,
the decision is primarily based on market sentiment, price movement analysis,
and reflections on past decisions, all pointing towards a precautionary SELL.
4. The mixed signals from trading strategies present an uncertain direction
for the
stock’s␣immediate␣future.␣Without␣a␣strong␣indication␣for␣a␣BUY␣or␣continuation␣to␣HOLD,␣the␣prudent␣course␣of␣action␣is␣to␣SELL.␣5.␣Lastly,␣financial␣constraints␣that␣limit␣further␣buying␣when␣cash␣reserves␣are␣low␣also␣support␣a␣SELL␣position␣to␣avoid␣liquidity␣issues.␣Collectively,␣these␣points␣direct␣the␣decision␣towards␣selling␣the␣current␣holdings␣with␣the␣aim␣of␣avoiding␣potential␣medium␣to␣long-
term␣losses,␣despite␣any␣short-term␣bullish␣trends.’

##### Example 3

⬇

1Action: SELL

2Reasoning: My analysis reveals mixed market sentiment both in the short and
long term, with significant immediate negative challenges likely impacting
AAPL’s␣price␣adversely.␣Despite␣professional␣investment␣guidance␣indicating␣technical␣strength␣and␣long-
term␣price␣movement␣analysis␣suggesting␣a␣bullish␣trend,␣the␣immediate␣negative␣factors␣outweigh␣the␣potential␣for␣ongoing␣gains.␣A␣critical␣factor␣influencing␣the␣decision␣is␣the␣limited␣cash␣reserve,␣which␣not␣only␣prevents␣buying␣at␣the␣current␣stock␣price␣but␣also␣underscores␣the␣need␣for␣liquidity␣and␣risk␣mitigation.␣Past␣trading␣decisions␣highlight␣the␣importance␣of␣adjusting␣positions␣in␣response␣to␣prevailing␣market␣conditions,␣even␣when␣they␣suggest␣a␣departure␣from␣an␣overall␣bullish␣sentiment.␣The␣absence␣of␣a␣clear␣signal␣from␣the␣provided␣trading␣strategies␣further␣supports␣a␣shift␣towards␣a␣SELL␣decision.␣My␣reasoning␣is␣grounded␣in␣a␣comprehensive␣analysis␣of␣market␣intelligence,␣market␣trends,␣professional␣investment␣guidances,␣trading␣strategies,␣and␣financial␣constraints,␣all␣of␣which␣collectively␣point␣to␣SELL␣as␣the␣prudent␣action␣under␣current␣circumstances.’

FinAgent’s decision-making prowess showcases a sophisticated blend of
comprehensive analysis, adaptability, and rigorous risk management, each
playing a pivotal role in navigating the intricate dynamics of financial
markets. The methodology is rooted in a deep analysis that encompasses an
array of factors such as market intelligence, price trends, and expert
investment advice. This holistic approach enables FinAgent to distill
actionable insights from complex data streams, as demonstrated in Example 1
where the BUY decision was informed by a confluence of positive market
sentiment toward Apple’s innovative AR/VR product line and a bullish price
trajectory across different timelines.

The essence of FinAgent’s strategy lies in its remarkable adaptability and
commitment to learning from historical trading patterns. This aspect is
vividly illustrated in Example 2, where a SELL decision was guided by an
analysis revealing a negative medium to long-term market sentiment,
underscoring FinAgent’s capacity to preemptively adjust its positions in
anticipation of future market movements.

Correcting the earlier oversight, Example 3 indeed underscores a SELL
decision, driven by a nuanced evaluation of mixed market sentiments, immediate
negative challenges potentially impacting AAPL’s stock price, and the critical
constraint of limited cash reserves. This example highlights FinAgent’s
strategic foresight and prudence. Despite recognizing the technical strength
and a bullish trend in the long-term price movement analysis, FinAgent opts to
sell, prioritizing liquidity and risk mitigation over speculative gains in the
face of significant immediate uncertainties and financial limitations.

In conclusion, FinAgent’s strategic approach to decision-making, illustrated
through these examples, emphasizes the importance of a balanced, informed, and
cautious investment strategy. By synthesizing comprehensive market analysis,
drawing lessons from past trading experiences, and meticulously managing
risks, FinAgent adeptly maneuvers through market volatility. This disciplined
methodology not only optimizes potential returns but also diligently minimizes
exposure to adverse market swings, demonstrating the criticality of
adaptability and prudent risk assessment in achieving sustainable investment
success.

###  I.2. Case Study: II

Figure 8. Performance analysis of FinAgent for GOOGL.

##### Example 1

⬇

1Action: BUY

2Reasoning: The decision to BUY is influenced by the summary of market
intelligence, which leans toward a positive long-term outlook for Alphabet
Inc., indicating strengthened areas like cloud and AI sectors, and persistent
investor confidence, despite short-term concerns. Reflecting on past price
movements and the upward trend in the latest short-term analysis confirms the
bullish momentum, which aligns with the past lessons on the importance of
acting on bullish signals and positive sentiment. Additionally, the reflection
on past trading decisions advises us not to miss out on BUY opportunities when
the overall trend and market sentiment are positive. Finally, with the absence
of any current position and sufficient cash reserves, the constraints are
satisfied for making a purchase decision. Therefore, taking into account the
bullish market outlook, positive price trend, and our capacity to act, the
correct decision at this point is to BUY.

##### Example 2

⬇

1Action: SELL

2Reasoning: Evaluation of the current
market’s␣risk␣of␣correction␣or␣downturn,␣a␣strategic␣SELL␣decision␣is␣considered.␣The␣decision␣to␣sell␣is␣further␣bolstered␣by␣the␣lack␣of␣liquidity␣to␣purchase␣additional␣shares␣and␣the␣desire␣to␣capitalize␣on␣the␣current␣stock␣value␣before␣any␣potential␣market␣adjustments␣that␣could␣erode␣gains.␣Additionally,␣reflections␣on␣past␣trading␣strategies␣underscore␣the␣risk␣of␣missing␣optimal␣selling␣points␣during␣bullish␣trends␣due␣to␣an␣overly␣conservative␣approach.␣Consequently,␣in␣light␣of␣the␣existing␣market␣optimism,␣the␣choice␣to␣SELL␣aims␣to␣preemptively␣secure␣profits␣and␣improve␣liquidity,␣providing␣a␣more␣flexible␣stance␣to␣navigate␣future␣market␣uncertainties␣and␣capitalize␣on␣buying␣opportunities␣at␣lower␣prices.␣This␣action␣respects␣financial␣limitations␣while␣strategically␣positioning␣for␣potential␣market␣recalibrations.’

##### Example 3

⬇

1Action: BUY

2Reasoning: The market intelligence presents a nuanced picture, highlighting
short-term challenges alongside significant long-term opportunities,
particularly with advancements in AI and a relaxation of regulatory pressures.
This analysis points to a mild bearish trend in the short to medium term but
underscores the positive long-term implications of these technological and
regulatory developments. Past reflections on trading decisions emphasize the
importance of seizing bullish momentum when it aligns with long-term growth
prospects. Contrary to previous constraints, my current cash reserve is ample,
exceeding the Adj Close Price, which positions me well to take advantage of
the buying opportunity. Given the
market’s␣recent␣positive␣response,␣indicating␣early␣recognition␣of␣the␣long-
term␣potential,␣there’s a clear rationale for a BUY decision. This action is
not only feasible due to the sufficient cash reserves but is also
strategically sound, aiming to capitalize on the anticipated long-term
appreciation. The decision to BUY, therefore, aligns perfectly with both the
financial capability to make such a purchase and the optimistic long-term
outlook. This strategic move is intended to pre-emptively lock in potential
long-term gains before these prospects are fully priced into the market,
leveraging the current financial flexibility to strengthen the
portfolio’s␣position␣for␣future␣growth.’

The decision-making approach of FinAgent, as showcased through the examples,
underscores a comprehensive and nuanced understanding of the market,
demonstrating several core strengths that contribute to its effective trading
strategy.

Firstly, in Example 1, FinAgent’s decision to BUY is informed by a detailed
analysis of market intelligence that points to a positive long-term outlook
for Alphabet Inc. This example highlights FinAgent’s ability to integrate
diverse sources of market intelligence, including sector-specific developments
in cloud and AI, and investor sentiment, into its decision-making process. It
reflects a strategic consideration of both current market conditions and
future growth prospects, showcasing the importance of acting on bullish
signals when the overall market sentiment and trend analysis suggest potential
for appreciation.

Example 2 presents a scenario where FinAgent opts for a SELL decision,
emphasizing risk management and liquidity considerations. The reasoning behind
the sell decision illustrates FinAgent’s capacity to preemptively navigate
potential market downturns or corrections by capitalizing on current stock
values. This decision is particularly noteworthy for its forward-looking
nature, aiming to secure profits and improve liquidity in anticipation of
future market uncertainties. It demonstrates FinAgent’s adaptability and its
ability to incorporate reflections on past trading strategies to avoid missing
optimal selling points, underscoring a proactive approach to risk management.

In Example 3, the decision to BUY despite short-term challenges, based on the
strength of long-term opportunities, particularly advancements in AI and
regulatory relaxations, showcases FinAgent’s strategic foresight. This example
illustrates how FinAgent leverages comprehensive market analysis to identify
and act on long-term growth prospects, even in the face of temporary market
downturns. The ample cash reserve facilitates this decision, highlighting the
importance of financial flexibility in capitalizing on strategic buying
opportunities.

Collectively, these examples demonstrate FinAgent’s strengths in leveraging
detailed market analysis, integrating short and long-term market trends,
managing risks proactively, and maintaining financial flexibility to make
informed trading decisions. FinAgent is characterized by a balance of
strategic foresight, adaptability, and a disciplined consideration of past
trading reflections, positioning it to capitalize on market opportunities
while navigating potential risks effectively.

###  I.3. Case Study III

Figure 9. Performance analysis of FinAgent for ETHUSD.

##### Example 1

⬇

1Action: SELL

2Reasoning: The decision to SELL the ETHUSD pair is a strategic outcome of
integrating market intelligence, historical trend analysis, technical
indicators, and risk management principles, revealing a comprehensive picture
of the
market’s␣downward␣trajectory.␣Negative␣sentiment␣prevails␣in␣the␣short␣to␣medium␣term,␣suggesting␣further␣depreciation,␣a␣view␣supported␣by␣consistent␣downtrends␣in␣historical␣price␣movements.␣Technical␣analyses,␣including␣the␣Moving␣Average␣(MA5)␣and␣Bollinger␣Bands,␣underscore␣the␣bearish␣momentum,␣with␣the␣ETHUSD␣pair’s
position near the lower Bollinger Band indicating not just an oversold state
but the persistence of the bearish trend. Financial considerations, especially
the current
position’s␣loss␣and␣insufficient␣cash␣reserves␣for␣additional␣purchases,␣necessitate␣a␣sell␣to␣prevent␣further␣losses.␣This␣decision,␣shaped␣by␣lessons␣from␣past␣trades␣and␣the␣observed␣continuity␣of␣the␣downtrend,␣aligns␣with␣a␣strategy␣focused␣on␣capital␣protection␣and␣risk␣management␣amidst␣negative␣market␣sentiment.␣Thus,␣selling␣is␣identified␣as␣the␣most␣prudent␣action␣to␣mitigate␣risks␣and␣capitalize␣on␣informed␣market␣analysis.’

##### Example 2

⬇

1Action: BUY

2Reasoning: The decision to BUY ETH/USD is strongly supported by a blend of
market intelligence indicating long-term positive sentiment, technical
analysis via MA5 and Bollinger Bands, and the advantage of having sufficient
cash reserves. The MA5 confirms the
asset’s␣robust␣momentum␣by␣showcasing␣its␣price␣consistently␣above␣this␣short-
term␣moving␣average,␣highlighting␣a␣sustained␣bullish␣trend.␣Concurrently,␣Bollinger␣Bands␣signal␣the␣asset’s
strength and potential for further growth as it trends towards or breaches the
upper band, indicative of high market performance and volatility in favor of
an upward trajectory. This comprehensive analysis, coupled with the financial
readiness to invest, aligns with an aggressive strategy to capitalize on the
anticipated bullish market dynamics. The convergence of positive long-term
outlooks, technical indicators underscoring the continuation of upward trends,
and liquidity positions BUY as a strategically sound decision aimed at
leveraging expected market gains.

The decision-making capabilities of FinAgent, as illustrated through examples,
reveal a sophisticated blend of strategic insight and analytical depth in
navigating the cryptocurrency market. This approach leverages comprehensive
market analysis, integrating market intelligence, historical trends, and
technical indicators like the Moving Average (MA5) and Bollinger Bands, to
inform its trading decisions.

Example 1 showcases FinAgent’s methodical use of data to make a SELL decision
on the ETHUSD pair, reflecting a commitment to capital protection and risk
management amidst negative market trends. This demonstrates FinAgent’s
strength in crafting informed strategies that prioritize long-term capital
security over short-term gains, illustrating a prudent approach to trading in
volatile markets.

Example 2 illustrates the proactive side of FinAgent, where a BUY decision is
made based on a positive market outlook. This decision, supported by solid
technical analysis and sufficient liquidity, shows FinAgent’s ability to seize
market opportunities, highlighting its adaptability and aggressive strategy to
leverage potential market upswings for significant gains.

FinAgent’s strategic and analytical framework, as evidenced by these examples,
effectively balances risk and reward, demonstrating a nuanced understanding of
the cryptocurrency market’s complexities. The methodical integration of
diverse data points into coherent trading strategies underscores FinAgent’s
capability to navigate market dynamics adeptly.

While the cryptocurrency market’s inherent volatility poses challenges,
FinAgent’s approach exhibits a level of resilience. The firm’s strategic
decisions, though cautious, are not overly conservative but are designed to
adapt to market conditions, seeking to optimize outcomes within the realm of
calculated risk. Moving forward, refining analytical models and strategies to
further align with the unpredictable nature of cryptocurrencies remains an
area for gradual enhancement. By continuing to evolve its decision-making
framework, FinAgent aims to maintain its strategic edge while mitigating the
impacts of market volatility, positioning it well for sustained success in the
dynamic landscape of cryptocurrency trading.

##  Appendix J Details of Benchmark Analysis

###  J.1. Analysis of Rule-based Strategies for Trading

We examine four fundamental rule-based strategies using technical indicators,
chosen for their broad coverage of market scenarios and strategic diversity in
trading. Moving Average Convergence Divergence (MACD) and stochastic
oscillators combined with Bollinger Bands represent trend-following methods
fundamental to capturing market momentum. In contrast, KDJ with RSI Filter and
Z-score Mean Reversion offer insights into potential market reversals, crucial
for risk management and exploiting countertrend opportunities. Together, these
strategies encompass a wide spectrum of trading situations, from following
prevailing trends to identifying reversal points, thereby providing a
comprehensive decision-making toolkit. To set a benchmark, these strategies
use the same market state and trading environment as other RL benchmark
methods. In the training stage, we applied OPTUNA for hyperparameter
optimization to adapt them to the financial instrument traded.

####  J.1.1. Moving Average Convergence Divergence (MACD) Crossover Strategy.

This strategy utilizes MACD indicators for trend-following, generating
buy/sell signals based on bullish or bearish momentum shifts, making it
suitable for moderate-risk traders favoring clear market trends.

Figure 10. Performance analysis of MACD for AAPL.

Figure 11. Performance analysis of MACD for GOOGL.

Figure 12. Performance analysis of MACD for ETHUSD.

Figure 13. Performance analysis of MACD for MSFT.

Limitation 1: Sub-optimal position-changing points. The MACD strategy often
exhibits delayed reactions to market changes, leading to sub-optimal position-
changing points. This lag is particularly pronounced in fast-moving or
volatile markets. For example, in the case of stocks like AAPL as marked in
Fig. 11, where market dynamics can shift rapidly, the MACD might signal a buy
or sell too late, thereby missing the optimal entry or exit points. This
limitation is intrinsic to the MACD’s reliance on historical moving averages,
making it less effective in markets that experience quick reversals or where
the trend direction changes frequently.

Limitation 2: Incapability of handling volatility. The MACD strategy struggles
in highly volatile markets due to its dependency on moving averages, which may
not accurately reflect current market conditions under such circumstances. In
volatile trading environments like the marked period in Fig.11, the MACD can
generate misleading signals, leading to a series of unprofitable trades. The
standard signal thresholds of MACD may not be adaptable enough for all market
conditions, particularly in volatile settings. This limitation underscores the
need for a more dynamic approach, possibly integrating adaptive thresholds
that account for the prevailing market volatility.

Limitation 3: High turnover rate results in high transaction fee. A
significant drawback of the MACD strategy is its tendency to generate a high
turnover rate, as shown in the marked periods of Fig.13 and Fig.13, leading to
substantial transaction costs. In financial markets where transaction fees are
considerable, like the stock market, frequent trading by MACD can
significantly lower profit margins.

####  J.1.2. KDJ&RSI Strategy.

This strategy merges the KDJ stochastic oscillator and RSI to identify
overbought or oversold conditions, issuing buy or sell signals based on
momentum and RSI thresholds, ideal for risk-averse traders preferring extreme
market conditions.

Figure 14. Performance analysis of KDJ&RSI for AAPL.

Figure 15. Performance analysis of KDJ&RSI for TSLA.

Limitation 1: Lack of generalization ability. The KDJ&RSI strategy, while
useful in certain market conditions, is not a universally applicable strategy.
It lacks generalization across different market environments due to its
sensitivity to specific parameters and market trends. This means that its
performance can vary greatly between different stocks. Despite the hyper-
partmeters are trained with historical data, it is still not able to get
profitable results on some instruments as shown in Fig. 15, 15.

####  J.1.3. Z-score Mean Reversion.

This strategy employs z-score mean reversion, generating buy or sell signals
when prices deviate significantly from the mean, making it suitable for risk-
averse traders who capitalize on price normalization.

Figure 16. Performance analysis of ZMR for AAPL.

Figure 17. Performance analysis of ZMR for ETHUSD.

Limitation 1: Sub-optimal decision due to insensitivity to market trend. The
ZMR trading strategy, while adept at capturing dramatic shifts in market
patterns, often exhibits a notable insensitivity to more stable, long-term
market trends. This limitation becomes particularly apparent in its tendency
to make sub-optimal trading decisions during periods of gradual market
changes. A clear example of this can be observed in the trading behavior of
AAPL, as depicted in the marked period of Fig. 17. The strategy decided to
sell after the price met the reverting point, missing the bullish trend
afterward. The strategy’s focus on mean reversion overlooks the significance
of persistent trend movements, leading to sub-optimal trading outcomes.

Limitation 2: Poor performance in non-mean-reverting Market conditions. A
significant limitation of the z-score mean-reversion trading strategy is its
poor performance in market conditions that do not exhibit mean-reverting
behavior. The strategy inherently assumes that stock prices will revert to
their historical mean, which is often not the case in trending or momentum-
driven markets. In such environments, where prices consistently move in one
direction without reverting, the mean-reversion strategy can result in
sustained losses. This is particularly evident in strongly bullish or bearish
markets, where the strategy may continuously take counter-trend positions,
leading to adverse trading outcomes. The inability of the mean-reversion
strategy to adapt to non-mean-reverting market conditions highlights its lack
of versatility and the potential risk of relying solely on historical mean-
reversion patterns. This is evident by the trading decisions in TSLA as shown
in Fig. 17 .

###  J.2. Analysis of Using FinAgent as a router for technical indicators-
driven strategies for trading.

We examined the potential of FinAgent to function as decision-making routers
within a Mixture-of-Experts (MoE) framework of trading strategies. This
experiment setting only utilized the decision module of FinAgent,
intentionally excluding components such as market intelligence, memory, and
reflection modules. This exclusion eliminates the necessity for a training
phase for FinAgent. The strategies implemented in this research have undergone
fine-tuning using Optuna on a designated training dataset. Each strategy’s
effectiveness is meticulously documented and provided to the LLM, enabling it
to make informed decisions based on the detailed performance data.

According to the numerical data presented in Table 4, the FinAgent router,
while not always achieving the optimal strategy outcome, consistently delivers
robust and positive trading results. This observation is further substantiated
by the analysis of trading behavior across various assets, which exhibits less
extreme behavior than using a single trading strategy. This suggests that the
FinAgent router is capable of adapting its decision-making process to various
market conditions. Although there is an improvement compared to employing
single trading strategies, we have also identified certain limitations. These
limitations are further addressed by other modules within the FinAgent system.

Figure 18. Performance analysis of using FinAgent as a strategy router for
ETHUSD.

Figure 19. Performance analysis of using FinAgent as a strategy router for
AAPL.

Limitation 1: Gap in using multiple strategies to make a single decision In
the role of a router, FinAgent demonstrates variable preferences for strategy
decisions at each stage. This variability introduces a potential risk of
unprofitable trading in such sequential decision-making scenarios, as each
strategy operates based on unique assumptions. For instance, consider the
period highlighted in Fig. 19. Within this short timeframe, multiple buy and
sell decisions are executed. The buy decisions are mostly influenced by the
KDJ&RSI and SO&BB strategies, which signal a buying opportunity. On the other
hand, the sell decisions are primarily guided by the ZMR strategy, which
indicates a strong sell signal during the same period.

Limitation 2: Limited performance with weak strategies. The router’s
performance is constrained by the effectiveness of the underlying strategies.
This limitation becomes evident in scenarios where all strategies demonstrate
weak performance. An example of this can be observed during the period marked
in Fig. 19. As illustrated in Fig. 11, Fig. 15, and Fig. 17, none of the
strategies managed to generate a profit during this period. In the absence of
other FinAgent modules, such as reflection, the decision-making capability of
the router is limited.

Limitation 3: Result is sensitive to prompt engineering and randomness. In our
experiments, we observed a distinct variation in the trading decisions when
modifying the decision prompt and executing the agent across different trials.
Owing to the inherent nature of the API service provided by OpenAI,
controlling the randomness in the responses is not feasible. This limitation
adds to the risk factor when relying solely on the decision module in FinAgent
for robustness.

###  J.3. Analysis of RL for Trading

Reinforcement learning has been applied to trading with varying degrees of
success. Traditional algorithms like PPO (Proximal Policy Optimization),
DQN(Deep Q-learning), and SAC(Soft Actor-Critic) have shown potential but also
limitations in stock prediction accuracy and practicality due to large data
requirements. To set a benchmark, these algorithms are trained in uniform
conditions and same dataset, using OPTUNA for hyperparameter optimization, to
explore their effectiveness in trading and provide insights into their
applicability and optimization for investment decision-making.

####  J.3.1. Analysis of PPO for trading

The Proximal Policy Optimization (PPO) model, when applied to stock trading,
exhibits notable drawbacks that can impact its overall performance. One
significant limitation is the tendency for the model to remain idle for
extended periods. This proclivity for inactivity stems from the algorithm’s
cautious learning process, prioritizing risk avoidance over seeking potential
gains. Consequently, during these prolonged idle phases, the model may miss
lucrative trading opportunities, leading to suboptimal returns. This issue
becomes particularly pronounced in dynamic and rapidly changing stock market
conditions, where the hesitancy of the PPO model may hinder its ability to
capitalize on favorable market movements. The model’s conservative nature
becomes particularly evident in Figure 21, where it remains idle even as the
cumulative return drops below 0, highlighting its reluctance to capitalize on
favourable market movements. This limitation underscores the need to refine
the PPO algorithm to balance risk management better and seize profitable
opportunities in dynamic market environments.

Figure 20. Performance analysis of PPO for AAPL.

Figure 21. Performance analysis of PPO for GOOGL.

Another drawback associated with PPO in stock trading lies in its challenge to
achieve optimal timing for buying and selling. The model may encounter
difficulties in accurately predicting short-term price movements, resulting in
purchasing stocks at relatively high prices or missing opportunities to buy at
lower prices. This suboptimal timing can directly impact the model’s overall
performance, leading to lower returns and less effective utilization of
available capital. The inherent uncertainty and volatility of stock markets
pose challenges for PPO models to consistently make well-timed decisions,
highlighting the complexity of accurately forecasting market movements. Figure
21 exemplifies this issue, where the model fails to capture the low-value on
September 27, 2023, resulting in a decision to buy at a relatively high price
and subsequently leading to suboptimal income. This highlights the need for
improved timing mechanisms within the PPO algorithm to enhance its
effectiveness in navigating the uncertainties of stock markets.

To address these drawbacks, it becomes imperative to fine-tune the model
parameters and optimize the training process. Additionally, augmenting the PPO
model with complementary techniques or incorporating more sophisticated
features, such as sentiment analysis and macroeconomic indicators, can enhance
its decision-making capabilities. Regular monitoring and adjustments are
crucial to adapting the model to changing market conditions, mitigating the
impact of extended idle periods, and improving its ability to time trades
effectively. By embracing a holistic approach that combines machine learning
with domain expertise, one can work towards overcoming these limitations and
developing a more robust and adaptive stock trading algorithm.

####  J.3.2. Analysis of DQN Algorithm for Stock Trading

Though the implementation of the Deep Q-Network (DQN) algorithm in stock
trading can yield relatively positive results in the long term, its
effectiveness in navigating market complexities has shown limitations.

Figure 22. Performance analysis of DQN for GOOGL.

Figure 23. Performance analysis of DQN for AMZN.

One primary drawback of the DQN algorithm is its inability to balance risk and
reward effectively. It often adopts a conservative stance, potentially leading
to missed opportunities for significant gains during bullish market phases.
Conversely, in bearish conditions, the model’s failure to swiftly adjust its
strategy can result in prolonged holding of depreciating assets, exacerbating
losses. This is evident in FigureLABEL:fig:_DQN_GOOGL, where the model fails
to capture the high-value point, selling at a lower price on a later day,
September 27, 2023, and missing the chance for better earnings. Additionally,
it takes no action and remains in a zero position during the bullish phase
from October 26, 2023, to November 23, 2023, missing this period of increase.
A similar issue occurs in the implementation of AMZN, missing the bullish
phase from October 23, 2023, to December 2023.

Another drawback of DQN in this context is its susceptibility to market
volatility and unpredictable nature, which can lead to suboptimal decision-
making. This issue is particularly noticeable in Figure 27, where DQN’s
performance in managing the ETHUSD asset demonstrates a pattern of delayed
reactions, often lagging behind rapid market movements, especially from July
18, 2023, to August 15, 2023, during periods of extreme price fluctuations.

To address these challenges, it is crucial to refine the DQN algorithm by
incorporating advanced features, such as sophisticated market indicators,
real-time data analysis, and diverse market data. Enhancing the algorithm’s
sensitivity to market dynamics and integrating effective risk management
strategies can improve its decision-making quality. Regular updates of the
model with current market data and trends are essential to maintain its
relevance and accuracy in a rapidly changing financial landscape.

####  J.3.3. Analysis of SAC Algorithm for Stock Trading

Figure 24. Performance analysis of DQN for ETHUSD.

Figure 25. Performance analysis of SAC for ETHUSD.

Figure 26. Performance analysis of SAC for MSFT.

Figure 27. Performance analysis of SAC for AMZN.

The Soft Actor-Critic (SAC) algorithm, when applied to stock trading,
demonstrates certain strengths but also faces challenges that limit its
performance. The introduction of entropy regularization enables the agent to
better explore unknown states, learning to formulate strategies through
interaction with the environment in order to maximize the expected cumulative
reward. However, it still encounters difficulties when dealing with the
intricacies of the stock market.

One limitation observed in the implementation of SAC is its handling of market
·volatility, especially in high-fluctuation environments like cryptocurrency
trading. For instance, when trade on Ethereum (ETHUSD), as depicted in Figure
27, SAC tends to be overly cautious. It performed two buy actions around Nov
15, 2023, during a bullish phase. These decisions were made over a relatively
long-term observation, which led to missed opportunities during significant
market movements. This conservative approach, though avoiding potential
losses, can result in the model not fully capitalizing on the profitable
market trends.

Similarly, in the stock market, SAC’s performance varies based on the specific
characteristics of each stock. For example, with Microsoft (MSFT) and Amazon
(AMZN), depicted in Figures 27 and 27, SAC struggles to optimally time its
entry and exit points. For AMAZ, it sold the holding stocks on October 19,
2023, missing several peaks and losing potential higher returns. For MSFT, it
performed sold operations on November 10, 2023, when the bullish market trend
still went on. Both late responses to market uptrends and premature exits from
profitable positions affect the overall return on trading.

In summary, while SAC offers a robust framework for reinforcement learning
tasks, its application in the dynamic and volatile stock requires careful
tuning and enhancement to better leverage its capabilities.

###  J.4. Analysis of FinGPT for Trading

FinGPT’s performance in predicting stock prices has exhibited several
significant flaws, highlighting critical limitations in its ability to provide
reliable insights for investment decisions.

Limitation 1: Failure to capitalize on peak price opportunities.  During a
specific period, FinGPT missed the chance to sell the stock at its zenith,
opting instead to hold onto it. Unfortunately, this strategy proved
detrimental as the stock’s value plummeted below the initial purchase price,
leading to avoidable financial losses. This reveals a deficiency in FinGPT’s
adaptive decision-making, as it neglected to adjust its strategy despite the
clear opportunity to maximize returns. For example, on July 10, 2023, FinGPT
made a buy action for MSFT at a price of $330.46. Despite reaching a peak
price of $358 on July 18, 2023, FinGPT continued to hold the stock, ultimately
making a misprediction about a further increase. The reasoning behind this
misprediction involved positive developments, market conditions, and the
anticipation of strong earnings. However, the model failed to adjust its
strategy and sold the stock at a lower price of $336.37 on July 26, 2023,
resulting in a significant loss as shown in Figure 31.

Limitation 2: Wrong prediction for a sharp price drop.  Another noted pattern
of inaccurate predictions by FinGPT in stock market forecasting raises serious
concerns about its reliability as a tool for investment guidance. The model’s
propensity to erroneously predict increases in stock prices, leading to
unexpected downturns, suggests a fundamental flaw in its predictive
capabilities, rendering it unsuitable for investors seeking profitable
opportunities.

A glaring example of FinGPT’s failure in predictive accuracy occurred in its
handling of TSLA (Tesla) stocks on October 8, 2023. The model recommended
purchasing TSLA stocks at a specific price of $245.34, operating under the
assumption that the stock would subsequently experience an upward trajectory.
However, this prediction proved grossly inaccurate, as the stock value
deviated significantly from the forecasted trajectory, plummeting to $215.49
by August 18, 2023. Figure 31 shows the details of this failure.

This significant misalignment between FinGPT’s predictions and the actual
market trajectory exposed its limitation in adapting to dynamic conditions and
eroded the confidence of investors who relied on its recommendations. The
incident highlights the imperative need for continuous improvement in FinGPT’s
algorithms to enhance its predictive accuracy, ensuring investors receive more
reliable guidance in fluctuating financial landscapes.

The inability of FinGPT to accurately anticipate the dynamic nature of
financial markets emphasizes the critical need for refining its predictive
algorithms. This refinement is crucial to ensure a more nuanced and accurate
understanding of market dynamics, ultimately enhancing the model’s reliability
as a tool for investment guidance. The incident with TSLA is a compelling
example of the imperative nature of continuous improvement in FinGPT’s
capabilities to meet the demands of a rapidly changing and unpredictable
financial landscape. Furthermore, this incident with TSLA serves as a poignant
reminder of the evolving challenges within the financial landscape,
necessitating ongoing efforts to fine-tune FinGPT’s algorithms.

Limitation 3: Wrong prediction for a stock price surge.  Conversely, when
FinGPT wrongly predicts a decrease in stock prices, it opts to sell, but the
actual market behaviour contradicts its forecast by demonstrating a continued
increase in stock value. This inconsistency in predicting market trends raises
questions about the model’s effectiveness in providing actionable insights
aligned with real-world financial dynamics. For example, on June 7, 2023, the
model recommended selling AAPL stocks, forecasting a 1-2% decrease based on
positive developments and potential concerns. The cited positive developments
included the expected valuation of $4 trillion by 2025 and the successful
launch of the Vision Pro. However, the market response contradicted FinGPT’s
projection, as the stock price increased for three consecutive days, as shown
in Figure 31. This highlights a substantial misalignment between the model’s
analysis and the market dynamics, exposing a critical flaw in its ability to
anticipate short-term stock movements accurately.

Figure 28. Performance analysis of FinGPT for AAPL.

Figure 29. Performance analysis of FinGPT for GOOGL.

Figure 30. Performance analysis of FinGPT for MSFT.

Figure 31. Performance analysis of FinGPT for TSLA.

Limitation 4: Mismatch between the action and reasoning. The further drawback
of FinGPT is its tendency to recommend continuous holding of stocks, even when
successful price predictions are made. Despite accurately forecasting market
movements, the model often fails to provide timely and proactive investment
strategies. This inclination towards recommending a prolonged holding strategy
may cause investors to miss out on valuable opportunities for profit
maximization. For example, during the period spanning from June 6, 2023, to
July 6, 2023, FinGPT showcased an impressive ability to predict all price
changes for GOOGL accurately. However, a critical flaw emerged in its
decision-making process, as the model consistently maintained the same
investment action without adapting to the foreseen market dynamics. Despite
believing that the stock was destined to experience a decline in the near
future, as shown in Figure 31, FinGPT failed to promptly translate this
insight into a strategic move such as selling the stock. This lack of agility
in responding to its predictions represents a significant shortcoming. The
essence of successful trading lies not only in the accurate anticipation of
market trends but also in the timely execution of appropriate actions to
capitalize on those predictions or, conversely, to mitigate potential losses.
FinGPT’s inability to recalibrate its investment strategy when confronted with
predicted downturns accentuates a key area where the model could significantly
improve its decision-making capabilities. While the model excels in
forecasting, integrating a more dynamic and proactive approach to trading
would enhance its overall efficacy. Financial markets are renowned for their
fluidity and susceptibility to rapid changes, making adaptability a crucial
element for success. The failure to adjust to evolving conditions hampers the
model’s ability to navigate market complexities and leads to missed
opportunities to leverage favourable price fluctuations. This limitation
underscores the critical necessity of incorporating a more responsive
decision-making approach into FinGPT’s functionality. The dynamic nature of
financial markets requires accurate predictions and the ability to interpret
changing conditions swiftly and proactively, ensuring that FinGPT remains a
valuable and reliable tool.

Table 8. AMZN wrong prediction for 3 consecutive days  Symbol | Date | Price Change | Action | Prediction
---|---|---|---|---
AMZN | 11/10 | -0.97 | HOLD | … increase by 0-1% …
AMZN | 14/10 | 3.21 | HOLD | … decrease by 0.5-1% …
AMZN | 15/10 | -2.6 | HOLD | … increase by 0-1% …

Limitation 5: Meaningless prediction after consecutive failures. The last
notable drawback of FinGPT is when FinGPT encounters three consecutive wrong
predictions. In response, the model generates explanations that lack substance
and offer little value to investors. These responses often involve generic
statements, such as ”it’s difficult to predict the exact stock price
movement,” accompanied by vague positive and negative factors. This tendency
to provide meaningless responses further diminishes FinGPT’s credibility and
utility in guiding investment decisions during challenging periods. For
example, The prediction FinGPT made on November 16, 2023, for AMZN, after the
wrong prediction from November 11, 2023, to November 15, 2023, as shown in
Table 8, instead of providing a meaningful analysis or adjusting its approach,
the model generated a generic response, acknowledging the difficulty of
predicting stock movements and offering vague positive factors and potential
concerns. This lack of nuanced reasoning and the provision of a seemingly
irrelevant response indicates a weakness in the model’s ability to learn from
its mistakes and adjust its predictions coherently.

###  J.5. Analysis of FinMem for Trading

FinMem primarily relies on layered memory to retrieve historical news and
financial reports, and depends on the reflection mechanism to contemplate the
potential connections between this information and future price movements. In
this section, we conduct a detailed analysis of the decision-making results
and reasoning provided by FinMem during the decision-making process.

Figure 32. Performance analysis of FinMem for AAPL.

Figure 33. Performance analysis of FinMem for MSFT.

Figure 34. Performance analysis of FinMem for TSLA.

Figure 35. Performance analysis of FinMem for ETHUSD.

Limitation 1: Incorrect decision-making due to partially positive market news.
The wrong buying decision was generated on 4 August 2023 as shown in Figure
35. The overall AAPL stock price from 1 August 2023 to 20 August 2023 was a
continuous downward trend. On August 4, 2023, most of the news was negative
for AAPL. For example, ”Huawei’s upgraded mobile operating system might
intensify competition in the smartphone sector,” and ”Apple’s loss of $3T
market value due to lower iPhone demand,” etc. There were only two pieces of
positive news: ”Services revenue offsets iPhone sales drag in earnings
report,” and ”Subscription-based business model indicates growth potential.”
However, FinMem ignored the long-term downward trend and provided a buying
rationale based on the belief that the earnings report and subscription-based
business model could generate positive signals for stock price appreciation.
This erroneous buying decision led to a shift in returns from 6% to -3%.

Limitation 2: The cash is insufficient for more shares but provides a buying
decision result. As shown in the Figure 35, our setting is that buying means
going all in, while selling means going all out. In the chart, it can be seen
that there were two buying operations, and although the stock price declined,
the remaining cash was not sufficient to support further buying. FinMem lacks
a clear understanding of the environment and its own current situation, which
could lead to decisions that contradict reality. In FinAgent, we have a
specially designed state description iframe module to avoid this situation.

Limitation 3: Unstable decision-making. FinMem’s decisions are unstable, as
shown in Figure 35 and 35, where FinMem made numerous buy-then-sell and sell-
then-buy operations. Although in a stable market with minor price
fluctuations, this would not significantly impact the return rate, in a highly
volatile market, a single unstable operation could lead to substantial losses.
We analyze the main reasons for this phenomenon from two aspects. First,
market information is complex and often contains both positive and negative
news, making it difficult to clearly determine what causes stock price
fluctuations. Second, FinMem is unable to perceive the historical record of
past decisions. It cannot clearly identify the historical conditions of the
current choice of decision, leading to unstable decision-making.