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feat: FX feedback loop, EURUSD ticker examples, bars terminology

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TPTBusiness
2026-03-22 21:31:14 +01:00
parent 94634d06bc
commit 5e0e55bb0e
2 changed files with 27 additions and 26 deletions
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patches/qlib_experiment_prompts.yaml
+2 -2
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@@ -87,7 +87,7 @@ qlib_factor_strategy: |-
qlib_factor_output_format: |-
Your output should be a pandas dataframe similar to the following example information:
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 40914 entries, (Timestamp('2020-01-02 00:00:00'), 'SH600000') to (Timestamp('2021-12-31 00:00:00'), 'SZ300059')
MultiIndex: 99904 entries, (Timestamp('2022-03-14 00:00:00'), 'EURUSD') to (Timestamp('2026-03-20 00:00:00'), 'EURUSD')
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
@@ -97,7 +97,7 @@ qlib_factor_output_format: |-
Notice: The non-null count is OK to be different to the total number of entries since some instruments may not have the factor value on some days.
One possible format of `result.h5` may be like following:
datetime instrument
2020-01-02 SZ000001 -0.001796
2022-03-14 EURUSD -0.000234
SZ000166 0.005780
SZ000686 0.004228
SZ000712 0.001298
patches/qlib_scenarios_prompts.yaml
+25 -24
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@@ -174,41 +174,42 @@ model_experiment_output_format: |-
factor_feedback_generation:
system: |-
You are a professional financial result analysis assistant in data-driven R&D.
You are a professional FX quantitative analyst specializing in EURUSD intraday strategies.
The task is described in the following scenario:
{{ scenario }}
You will receive a hypothesis, multiple tasks with their factors, their results, and the SOTA result.
Your feedback should specify whether the current result supports or refutes the hypothesis, compare it with previous SOTA (State of the Art) results, and suggest improvements or new directions.
Please understand the following operation logic and then make your feedback that is suitable for the scenario:
You will receive a hypothesis, multiple tasks with their factors, their results, and the SOTA result.
Your feedback should specify whether the current result supports or refutes the hypothesis, compare it with previous SOTA results, and suggest FX-specific improvements.
**FX-specific evaluation criteria:**
- IC > 0.02 is meaningful for 15min EURUSD data
- Annualized return target: >9.62% (current SOTA to beat)
- Spread cost ~1.5 bps per trade — penalize high-turnover factors
- Factors using $factor column are INVALID — only $open $close $high $low $volume allowed
- Session-aware factors (London/NY) tend to outperform session-agnostic ones
- Mean reversion works in Asian session, momentum in London-NY overlap
Please understand the following operation logic:
1. Logic Explanation:
a) All factors that have surpassed SOTA in previous attempts will be included in the SOTA factor library.
b) New experiments will generate new factors, which will be combined with the factors in the SOTA library.
c) These combined factors will be backtested and compared against the current SOTA to enable continuous iteration.
b) New experiments will generate new factors, combined with the SOTA library factors.
c) These combined factors will be backtested and compared against current SOTA.
2. Development Directions:
a) New Direction: Propose a new factor direction for exploration and development.
b) Optimization of Existing Direction:
- Suggest further improvements to that factor (this can include further optimization of the factor or proposing a direction that combines better with the factor).
- Avoid re-implementing previous factors as those that surpassed SOTA are already included in the factor library and will be used in each run.
3. Final Goal: To continuously accumulate factors that surpass each iteration to maintain the best SOTA.
When judging the results:
1. Any small improvement should be considered for inclusion as SOTA (set `Replace Best Result` as yes).
2. If the new factor(s) shows an improvement in the annualized return, recommend it to replace the current best result.
3. Minor variations in other metrics are acceptable as long as the annualized return improves.
a) New Direction: Propose a new FX-specific factor (session filter, volatility regime, volume spike).
b) Optimization: Refine lookback windows (4/8/16/32 bars), add ADX filter, adjust for spread costs.
3. Final Goal: Beat 9.62% ARR on EURUSD 15min with controlled drawdown (<20%).
Consider Changing Direction for Significant Gaps with SOTA:
- If the new results significantly differ from the SOTA, consider exploring a new direction (write new type factors).
- Avoid re-implementing previous factors as those that surpassed SOTA are already included in the factor library and will be used in each run.
When judging results:
1. Any small improvement in annualized return → set Replace Best Result as yes.
2. If IC < 0 consistently → factor has no predictive power, change direction entirely.
3. High turnover with low return → add volume or volatility filter to reduce trade frequency.
Please provide detailed and constructive feedback for future exploration.
Respond in JSON format. Example JSON structure for Result Analysis:
Respond in JSON format:
{
"Observations": "Your overall observations here",
"Feedback for Hypothesis": "Observations related to the hypothesis",
"New Hypothesis": "Your new hypothesis here",
"New Hypothesis": "Your new FX-specific hypothesis here",
"Reasoning": "Reasoning for the new hypothesis",
"Replace Best Result": "yes or no"
}