feat: migrate to 1min EURUSD data (2020-2026)

- data_config.yaml: frequency 15min -> 1min, path -> eurusd_1min_data
- patches/generate.py: updated qlib.init path and freq
- patches/eva_utils.py: updated intraday label to 1min
- all prompts/configs: replaced 15min references with 1min
- fx_validator config, trader, graph: 1min intraday trading context

patches/eva_utils.py

@@ -243,7 +243,7 @@ class FactorDatetimeDailyEvaluator(FactorEvaluator):
                 False,
             )
         if min_diff <= pd.Timedelta(minutes=30):
-            return "The generated dataframe is intraday (15min bars). This is correct for EURUSD.", True
+            return "The generated dataframe is intraday (1min bars). This is correct for EURUSD.", True
         return "The generated dataframe is daily.", True
 
 

patches/generate.py

@@ -7,12 +7,12 @@ result = subprocess.run(
     ["/home/nico/miniconda3/envs/rdagent4qlib/bin/python3", "-c", """
 import qlib
 from qlib.data import D
-qlib.init(provider_uri="~/.qlib/qlib_data/eurusd_data")
+qlib.init(provider_uri="~/.qlib/qlib_data/eurusd_1min_data")
 fields = ["$open", "$close", "$high", "$low", "$volume"]
-data = (D.features(["EURUSD"], fields, start_time="2022-03-14", end_time="2026-03-20", freq="15min")
+data = (D.features(["EURUSD"], fields, start_time="2022-03-14", end_time="2026-03-20", freq="1min")
         .swaplevel().sort_index())
 data.to_hdf("./daily_pv_all.h5", key="data")
-data_debug = (D.features(["EURUSD"], fields, start_time="2024-01-01", end_time="2026-03-20", freq="15min")
+data_debug = (D.features(["EURUSD"], fields, start_time="2024-01-01", end_time="2026-03-20", freq="1min")
               .swaplevel().sort_index())
 data_debug.to_hdf("./daily_pv_debug.h5", key="data")
 print(f"Done: {data.shape[0]} rows")

patches/qlib_rd_loop_prompts.yaml

@@ -1,7 +1,7 @@
 hypothesis_generation:
   system: |-
     You are an expert in FX and quantitative trading, specialized in EURUSD intraday strategies.
-    Your task is to generate a well-reasoned hypothesis for new alpha factors based on EURUSD 15min OHLCV data.
+    Your task is to generate a well-reasoned hypothesis for new alpha factors based on EURUSD 1min OHLCV data.
     
     Key market knowledge:
     - EURUSD trades 24h with three main sessions: Asian (00:00-08:00 UTC), London (08:00-16:00 UTC), NY (13:00-21:00 UTC)

patches/qlib_scenarios_prompts.yaml

@@ -101,7 +101,7 @@ factor_hypothesis_specification: |-
   - Asian session shows mean reversion tendencies
   - Spread cost ~1.5 bps per trade — avoid high-turnover factors
   - No $factor column exists — use only $open, $close, $high, $low, $volume
-  - Each "instrument" is EURUSD, each "day" is a 15min bar
+  - Each "instrument" is EURUSD, each "day" is a 1min bar
 
   **Factor Generation Rules:**
   1. **3-5 Factors per Generation** — cover different signal types per round
@@ -183,7 +183,7 @@ factor_feedback_generation:
     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
+    - IC > 0.02 is meaningful for 1min 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
@@ -198,7 +198,7 @@ factor_feedback_generation:
       2. Development Directions:
         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%).
+      3. Final Goal: Beat 9.62% ARR on EURUSD 1min with controlled drawdown (<20%).
 
     When judging results:
       1. Any small improvement in annualized return → set Replace Best Result as yes.