kafal 1.0.0

Embeddable DSL for trading quant models, research factors, and backtesting.

kafal

kafal is a compact scripting layer for building trading indicators, strategies, and research factors on top of pandas OHLCV data.
It sits between raw Python and chart / execution UIs:

• DSL for signals, risk, and execution.
• TA + quant primitives (TA‑Lib wrappers, rolling stats, z‑scores, etc.) wired straight into your script.
• A configurable strategy engine with realistic fills, intrabar simulation, and portfolio aggregation.

Write strategy logic fast, run it anywhere Python runs.

Status: alpha / experimental – API is stabilising; small breaking changes may still happen before 1.0.

---

60‑second Quickstart

Install:

pip install kafal

1. Indicator + crossover strategy in one script

import pandas as pd
from kafal.core.interpreter import KafalInterpreter

Create a toy OHLCV DataFrame:

dates = pd.date_range("2025-01-01", periods=200, freq="1H")
close = pd.Series(100 + 0.1 * range(200), index=dates)

df = pd.DataFrame(
    {
        "open": close.shift(1).fillna(close.iloc),
        "high": close + 1.0,
        "low": close - 1.0,
        "close": close,
        "volume": 1_000,
    },
    index=dates,
)

Kafal script (as a string):

code = """
// Simple EMA crossover strategy

len_fast = 10
len_slow = 30

fast = ema(close, len_fast)
slow = ema(close, len_slow)

plot(close) - title="Close"
plot(fast)  - title="Fast EMA"  color=rgba(0,255,0,0.9)
plot(slow)  - title="Slow EMA"  color=rgba(255,0,0,0.9)

long_cond  = crossover(fast, slow)
short_cond = crossunder(fast, slow)

// Modern trading API (preferred)
trade.long(long_cond,  size=1.0)
trade.short(short_cond, size=1.0)
"""

Run it:

intr = KafalInterpreter()
result = intr.run(code, df, mode="strategy")

plots    = result["plots"]
strategy = result["strategy"]
equity   = strategy["equity"]
trades   = strategy["trades"]
stats    = strategy["stats"]

• Feed plots into your chart layer.
• Use equity / trades / stats for a basic backtest view.

2. Strategy with inputs and the modern trade.* API

indicator_code = """
define name="EMA Cross (Inputs)" overlay=true category="trend"

input fast_len:int 10  min=2  max=50  step=1  title="Fast EMA"
input slow_len:int 30  min=5  max=200 step=1  title="Slow EMA"

fast = ema(close, fast_len)
slow = ema(close, slow_len)

plot(fast) - color=rgba(0,255,0,0.9) title="Fast"
plot(slow) - color=rgba(255,0,0,0.9) title="Slow"

long_cond  = crossover(fast, slow)
short_cond = crossunder(fast, slow)

trade.long(long_cond,  size=1.0)
trade.short(short_cond, size=1.0)
"""

intr = KafalInterpreter()
intr.set_inputs({"fast_len": 12, "slow_len": 34})

result = intr.run(indicator_code, df, mode="strategy")

strategy      = result["strategy"]
stats         = strategy["stats"]
inputs_schema = result["inputs_schema"]

• inputs_schema tells your UI which sliders / controls to render.
• set_inputs lets your app push UI values back into the script.

For more copy‑pasteable scripts, see:

• docs/Quickstart.md
• docs/Indicator_cookbook.md
• docs/Strategy_cookbook.md

---

Public API surface (host‑facing)

Core class

from kafal.core.interpreter import KafalInterpreter

Typical construction:

intr = KafalInterpreter(
    host_api=None,           # dict of data callbacks (see Host API)
    max_request_calls=50,    # safety cap for cross-symbol requests
    initial_equity=100_000.0,
    commission_perc=0.0,
    slippage=0.0,
    pyramiding=1,
    execution_mode="backtest",   # "backtest" | "realistic"
    symbol_configs=None,         # per-symbol dealing config
    intrabar_model="none",       # "none" | "random_walk"
    intrabar_steps=0,            # synthetic ticks per bar if intrabar_model != "none"
)

Modes

result = intr.run(code, df, mode="chart")     # visuals + optional strategy
result = intr.run(code, df, mode="strategy")  # same payload, host treats as strategy
result = intr.run(code, df, mode="research")  # focus on features; visuals optional

---

Host API (data access)

All external data is explicit and host‑controlled, via a small host API.

• request_security(symbol, timeframe, field_or_expr, gaps="off" | "ffill" | "on")
• host_api["request_security"] / "requestsecurity" → v1 per‑field API (returns Series).
• host_api["request_ohlcv"] / "requestohlcv" → v2 OHLCV API (returns DataFrame).

Scripts never open files, hit the network, or touch OS APIs.

Minimal example:

import pandas as pd
from kafal.core.host_api import HostAPI
from kafal.core.interpreter import KafalInterpreter

def request_ohlcv(symbol: str, timeframe: str) -> pd.DataFrame:
    # You decide where data comes from (DB, gateway, CSV, etc.)
    df = load_ohlcv_from_somewhere(symbol, timeframe)
    # Must be a DataFrame with at least: open, high, low, close, volume
    return df

host_api = {"request_ohlcv": request_ohlcv}
intr = KafalInterpreter(host_api=host_api)

result = intr.run(code, df, mode="chart")

Inside the script:

// Uses host_api["request_ohlcv"] under the hood
other_close = request_security("NIFTY", "1D", "close")
spread      = close - other_close
plot(spread) - title="Spread vs NIFTY"

---

Sandbox & safety model

Kafal scripts are untrusted by default. The interpreter enforces:

• No filesystem, network, or OS access.
• __builtins__ cleared during eval.
• AST validator that blocks attribute access, imports, lambdas, comprehensions, etc.
• Loop bounds capped by MAX_FOR_RANGE.
• request_* calls limited via max_request_calls.

Host‑side, you only expose controlled callbacks via host_api; everything else is locked down by the sandbox.

---

What run() returns

result = intr.run(code, df, ...) yields a dict with these main fields:

• Visual layer

  • result["plots"]
  • result["shapes"]
  • result["hlines"]
  • result["bgcolors"]
  • result["fills"]
  • result["tables"]
  • result["alerts"]

• Strategy payload

  strat    = result["strategy"]
  equity   = strat["equity"]    # Series
  pnl      = strat["pnl"]       # Series
  position = strat["position"]  # Series
  trades   = strat["trades"]    # List[dict]
  stats    = strat["stats"]     # Dict[str, Any]
  

• Meta / research

  • result["inputs_schema"] – description of input.* fields.
  • result["meta"] – user metadata from define lines.
  • result["features"] – named research features (in research mode).
  • result["mode"] – echo of the mode you passed.

These keys are stable for the 0.1 line; new keys may be added in future versions.

---

DSL snapshot (what you can write)

Series and TA

mid    = hl2(high, low)
trend  = ema(close, 50)
vol_z  = zscore(volume, 50)

plot(mid)   - color=rgba(255,255,0,0.9) title="HL2"
plot(trend) - color=rgba(0,200,255,0.9) title="EMA 50"
plot(vol_z) - color=rgba(255,165,0,0.9) title="Vol zscore"

Control flow and arrays

sumval = 0
for i in 0..10 {
    if i > 5 {
        sumval = sumval + 1
    }
}

arr = array()
for i in 0..5 {
    array_push(arr, i)
}

plot(sumval) - title="Counter"

Functions and pipes

fn atr_band(src, len, mult) = src + atr(len) * mult

upper   = atr_band(close, 14,  2.0)
lower   = atr_band(close, 14, -2.0)
signal  = close | rsi(14) | sma(9)

plot(upper)  - title="ATR Upper"
plot(lower)  - title="ATR Lower"
plot(signal) - title="RSI SMA"

For a deeper walkthrough, see docs/Quickstart.md plus the indicator & strategy cookbooks.

---

Examples in this repo

You already have runnable examples wired to the interpreter and portfolio engine:

• examples/run_research_example.py – single‑symbol research mode.
• examples/run_universe_research.py – multi‑symbol research / factor panel.
• examples/run_strategy_example.py – single‑symbol strategy backtest.

Run them:

python examples/run_strategy_example.py
python examples/run_research_example.py
python examples/run_universe_research.py

These act as “host skeletons” you can copy into your own app.

---

Embedding Kafal in your app

1. Install & import

   pip install kafal
   

   from kafal.core.interpreter import KafalInterpreter
   

2. Provide data

   • Build a pandas DataFrame with open, high, low, close, volume.
   • Optionally implement request_ohlcv / request_security to support multi‑symbol or multi‑timeframe scripts.

3. Expose scripting

   • Let users upload .kf files or edit scripts in a text area.
   • Read into code: str, then call intr.run(code, df, host_api=..., mode=...).

4. Render outputs

   • Map plots, fills, shapes, hlines, bgcolors, tables to your chart library.
   • Use result["strategy"]["equity"], ["pnl"], ["trades"], ["stats"] for backtest panels.
   • Use result["features"] for factor research and ranking dashboards.

5. Lock in behaviour with tests

   • Mirror patterns from kafal/tests/test_kafal.py:
     • host API error behaviour you depend on.
     • "backtest" vs "realistic" execution.
     • presence and types of keys in run() output.
     • portfolio aggregation in PortfolioState.

Errors & troubleshooting

Kafal surfaces structured runtime errors to make debugging easier.

• All runtime errors are prefixed with Kafal: and carry a stable KAFAL-* code at the end.

  • Example: Kafal: execution_mode must be 'backtest' or 'realistic'. [KAFAL-EXEC-MODE]
  • Example: Kafal: host_api['request_ohlcv'] must return a pandas DataFrame. [KAFAL-HOST-OHLCV-TYPE]

• Codes are stable and searchable:

  • KAFAL-EXEC-* – interpreter configuration issues (e.g. bad execution_mode or run(mode=...)).
  • KAFAL-EXPR-* – DSL expression / sandbox issues (e.g. blocked .attr, lambdas, invalid operators).
  • KAFAL-HOST-* – host integration issues (e.g. missing or wrong‑type request_security / request_ohlcv).

If you see a Kafal error:

• Read the human message; it always includes what went wrong and usually how to fix.
• Use the KAFAL-* code in your logs, tests, or docs search to find the relevant contract or section.