What this tutorial covers

Step	What you learn
1	Build the runtime + transpile a Pine MACD into `strategy.so`.
2	Load `strategy.so` from Python via `ctypes`.
3	Push an OHLCV feed and call run_backtest_full.
4	Read every interesting field of `pf_report_t`.
5	Re-run the same compiled strategy with different parameters — no recompile.
6	Sweep a 2-D parameter grid in parallel using one `.so` per worker.

By the end you'll have the canonical patterns for ad-hoc backtests, parameter sweeps, walk-forward windows, and live diagnostic capture.

Layout

tutorial/
├── macd/
│   ├── strategy.pine       # PineScript v6 reference
│   └── generated.cpp       # transpiled C++ → becomes strategy.so
├── data/
│   ├── btcusdt_15m_7d.csv  # 672 frozen bars (Binance)
│   └── fetch_btcusdt.py    # refresh from Binance public API
├── run.py                  # ctypes harness
├── run_stream.py           # historical OHLCV → realtime trade stream
├── run_advanced.py         # parameter sweep using ABI overrides
├── run.sh                  # one-shot: cmake build + run.py
└── CMakeLists.txt

The Pine source

//@version=6
strategy("MACD Cross", overlay=false,
         initial_capital=10000, default_qty_type=strategy.percent_of_equity,
         default_qty_value=100, commission_type=strategy.commission.percent,
         commission_value=0.04)
 
fast   = input.int(12,  "Fast Length")
slow   = input.int(26,  "Slow Length")
signal = input.int(9,   "Signal Length")
 
[macd, sig, hist] = ta.macd(close, fast, slow, signal)
 
longCond  = ta.crossover(macd, sig)
shortCond = ta.crossunder(macd, sig)
 
if longCond
    strategy.entry("L", strategy.long)
if shortCond
    strategy.entry("S", strategy.short)

Path A — local toolchain

Requires cmake, g++, and python3.

bash tutorial/run.sh

Configures CMake (first time only), builds tutorial/macd/strategy.so, then runs the harness. Expected output:

MACD(12,26,9) on BTCUSDT 15m — 672 bars, 2026-04-29 18:15 → 2026-05-06 18:00 UTC
  trades:    49  (16W / 33L, 32.7% win)
  net pnl:   -190.85
  best/worst:+1149.00 / -1111.97
  max dd:    -4045.15
  elapsed:   0.4 ms

Numbers depend on the OHLCV snapshot — refresh with python3 tutorial/data/fetch_btcusdt.py to get current Binance bars.

Path B — Docker

Mount the strategy + OHLCV into the published runtime image; get a JSON report on stdout.

docker run --rm \
  -v "$(pwd)/tutorial/macd/generated.cpp:/in/strategy.cpp:ro" \
  -v "$(pwd)/tutorial/data/btcusdt_15m_7d.csv:/in/ohlcv.csv:ro" \
  ghcr.io/pineforge-4pass/pineforge-engine:latest > report.json
 
jq '.summary' report.json

Same engine, identical numbers. Build the image locally instead with docker build -t pineforge -f docker/Dockerfile . if you don't want to pull from GHCR.

Inside run.py — annotated walkthrough

The full harness is ~80 lines. Here's the dataflow, end to end.

1. Mirror the C ABI in ctypes

Skipped here — see FFI from Python for the complete mirror. The harness defines BarC, TradeC, and ReportC exactly matching pf_bar_t, pf_trade_t, pf_report_t.

2. Load OHLCV into a contiguous array

with OHLCV.open(newline="") as f:
    rows = list(csv.DictReader(f))
n = len(rows)
bars = (BarC * n)()
for i, r in enumerate(rows):
    bars[i] = BarC(float(r["open"]), float(r["high"]), float(r["low"]),
                   float(r["close"]), float(r["volume"]), int(r["timestamp"]))

(BarC * n)() allocates a contiguous block — the runtime walks it as pf_bar_t[] directly, no copying.

3. Wire the symbol table

lib = ctypes.CDLL(str(SO))
lib.strategy_create.argtypes  = [ctypes.c_char_p]
lib.strategy_create.restype   = ctypes.c_void_p
lib.run_backtest_full.argtypes = [
    ctypes.c_void_p, ctypes.POINTER(BarC), ctypes.c_int,
    ctypes.c_char_p, ctypes.c_char_p,
    ctypes.c_int, ctypes.c_int, ctypes.c_int,
    ctypes.POINTER(ReportC)]
lib.strategy_free.argtypes = [ctypes.c_void_p]
lib.report_free.argtypes   = [ctypes.POINTER(ReportC)]

Warning: Always set argtypes. Without them, Python silently passes int as 32-bit — your timestamps lose half their bits.

4. Run

state, report = lib.strategy_create(b"{}"), ReportC()
 
t0 = time.time()
lib.run_backtest_full(state, bars, n,
                      b"",      # input_tf — auto-detect
                      b"",      # script_tf — same as input
                      0, 4, 3,  # magnifier off, 4 samples, ENDPOINTS
                      ctypes.byref(report))
elapsed = time.time() - t0

5. Read the report

pnls = [report.trades[i].pnl for i in range(report.trades_len)]
wins, losses = sum(p > 0 for p in pnls), sum(p < 0 for p in pnls)
 
cum = peak = max_dd = 0.0
for p in pnls:
    cum += p
    peak = max(peak, cum)
    max_dd = min(max_dd, cum - peak)
 
print(f"  trades:    {report.trades_len}  ({wins}W / {losses}L)")
print(f"  net pnl:   {report.net_profit:+.2f}")
print(f"  max dd:    {max_dd:.2f}")

6. Free

lib.report_free(ctypes.byref(report))

lib.strategy_free(state)

Order matters — see Lifecycle § Free everything.

More worked examples

The four pages below pick up where this tutorial leaves off — each one is a self-contained, runnable example targeting a specific use case.

Example	What it shows
Pure C example	Same MACD run, no Python. End-to-end C code with `gcc` build.
Historical to realtime streaming	Warm the same MACD instance on OHLCV, then continue it on ordered trade ticks.
Parameter sweep in Python	Re-run one `.so` with a 2-D MACD grid. No recompile per run.
Multi-strategy harness	Load N `.so` files, run them in parallel against the same feed.
Magnifier on vs off	A/B comparison showing how intra-bar fills change the trade list.
Calling from Rust	Idiomatic safe Rust wrapper around the C ABI.

What to read next

Lifecycle — the four-step strategy-handle pipeline
Configuration — every override knob
Report schema — every field in pf_report_t
Bar magnifier — when and how to enable intra-bar fills
ABI stability — what you can rely on across versions

Table of Contents