release: v0.21.0

examples/18_csv_import.py

@@ -8,7 +8,7 @@ Demonstrates:
 The standard format is a header row + `timestamp,open,high,low,close,volume`
 where timestamp is Unix milliseconds. MT4/MT5 exports are auto-detected.
 
-Data: synthetic — a sample generated by this file, reproducible
+Data: synthetic (seed 18) — a sample generated by this file, reproducible
 
 Usage:
     python examples/18_csv_import.py
@@ -16,6 +16,8 @@ Usage:
 import os
 import tempfile
 
+import numpy as np
+
 import manifoldbt as mbt
 from manifoldbt.indicators import close, ema
 from manifoldbt.helpers import time_range, Interval
@@ -26,17 +28,31 @@ from manifoldbt.helpers import time_range, Interval
 tmp = tempfile.mkdtemp()
 csv_path = os.path.join(tmp, "SAMPLE_1m.csv")
 
+# 120 days of 1-minute bars. The span is the point: annualised metrics (CAGR,
+# Calmar) divide by the length of the window, so a fixture covering two days
+# reports a growth rate with fifty digits in it and teaches the reader nothing
+# except that nobody read the output.
+N = 120 * 1440
+rng = np.random.default_rng(18)
+
+steps = rng.normal(1.4e-6, 4.0e-4, N)          # ~+20% over the window
+px = 100.0 * np.exp(np.cumsum(steps))
+o = px
+c = np.roll(px, -1)
+c[-1] = px[-1]
+amp = np.abs(rng.normal(0.0, 2.0e-4, N))
+hi = np.maximum(o, c) * (1 + amp)
+lo = np.minimum(o, c) * (1 - amp)
+vol = rng.uniform(1_000, 5_000, N)
+
 base_ms = 1_704_067_200_000  # 2024-01-01 00:00 UTC
-px = 100.0
 with open(csv_path, "w") as f:
     f.write("timestamp,open,high,low,close,volume\n")
-    for i in range(3000):
-        ts = base_ms + i * 60_000               # 1-minute bars
-        nxt = px * (1.0 + (0.0009 if i % 3 else -0.0007))
-        hi = max(px, nxt) + 0.05
-        lo = min(px, nxt) - 0.05
-        f.write(f"{ts},{px:.4f},{hi:.4f},{lo:.4f},{nxt:.4f},{1000 + i}\n")
-        px = nxt
+    f.write("\n".join(
+        f"{base_ms + i * 60_000},{o[i]:.4f},{hi[i]:.4f},{lo[i]:.4f},{c[i]:.4f},{vol[i]:.0f}"
+        for i in range(N)
+    ))
+    f.write("\n")
 
 # -- 2. Import into the store (free, all tiers) -------------------------------
 store = mbt.import_csv(
@@ -59,7 +75,7 @@ strategy = (
     .describe("EMA(10/30) crossover on CSV-imported data")
 )
 
-start, end = time_range("2024-01-01", "2024-01-04")
+start, end = time_range("2024-01-01", "2024-05-01")
 config = mbt.BacktestConfig(
     universe=[1],
     time_range_start=start,
@@ -71,4 +87,6 @@ config = mbt.BacktestConfig(
 
 if __name__ == "__main__":
     result = mbt.run(strategy, config, store)
+    print("The series carries a mild upward drift, so the figures below")
+    print("describe the fixture. The mechanism on show is the import.\n")
     print(result.summary())