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Troubleshooting ​

Most common problems in backtesting, with root cause and fix. Ordered by frequency of occurrence.

"0 trades" ​

The backtest completes, but the trade list is empty.

Cause 1 - entry_conditions and on_bar_strategy together ​

The most frequent case. When entry_conditions is present in the DECLARATION and on_bar_strategy is also defined, the engine prioritizes declarative mode and ignores the imperative one. If the conditions do not have valid plots or the series does not fire, the result is zero trades.

python
# Incorrect
DECLARATION = {
    "entry_conditions": [{"action": "buy_to_open", "enabled": True, ...}],
    ...
}
def on_bar_strategy(sdk, params):
    sdk.buy(...)   # never runs

Fix: choose one mode and remove the other. Details in when to use declarative mode.

Cause 2 - Insufficient data for warmup ​

The strategy requires N candles, but the backtest has fewer than N. The script returns early on every bar:

python
if len(sdk.candles) < 200:  # 200-bar warmup
    return

Fix: reduce the minimum period or extend the backtest interval. For SMA 200, use at least 500 candles to obtain effective signals.

Cause 3 - Entry condition never true ​

The signal does not fire in the period. Possible reasons:

  • Thresholds too restrictive (e.g., RSI < 10, extremely rare).
  • Trend filter never aligned with the signal.
  • Bug in the condition (wrong operator, different series name).

Fix: add print() temporarily:

python
if sdk.position == 0:
    if rsi < oversold:
        print(f"SIGNAL fires: rsi={rsi}")
        sdk.buy(...)

If prints appear but trades do not, the issue is in sdk.position (position already open) or rejection due to balance.

Cause 4 - sdk.buy() without action= ​

Raises ProtocolError and halts execution. On the panel, the error is displayed. If the script uses only print() instead of sdk.buy(), the error does not occur.

Fix: see canonical actions.

"Only 1 trade" ​

One entry and one forced exit at the end of the period.

Cause - missing exit condition ​

The position is opened but there is no logic to close it. The backtest keeps the position open until the last candle, closing on termination.

python
# Incorrect: only enters, never exits
if sdk.position == 0 and crossed_up:
    sdk.buy(action="buy_to_open", qty=1, order_type="market")
# Missing: elif sdk.position > 0 and crossed_down: ...

Fix: add elif sdk.position > 0 and <condition>: sdk.sell(action="sell_to_close", ...).

"Positions accumulating" ​

The log shows several consecutive buy_to_open orders when one per signal is expected.

Cause - missing if sdk.position == 0: before entering ​

On every bar in which the condition is true, the script attempts to open. The engine rejects silently (Max Positions = 1), but the log becomes polluted.

python
# Incorrect
if fast_ma > slow_ma:
    sdk.buy(action="buy_to_open", qty=1, order_type="market")

# Correct
if sdk.position == 0 and fast_ma > slow_ma:
    sdk.buy(action="buy_to_open", qty=1, order_type="market")

Absurd metrics (Sharpe > 5, PF > 10) ​

Cause 1 - Look-ahead bias ​

Use of future data in the current decision. Classic example:

python
# Incorrect: uses high of the candle still closing
if sdk.candles[-1]["high"] > sdk.candles[-1]["close"]:
    sdk.buy(...)

The high of the current candle is only known at close, but the code makes the decision as if the value were available during the candle. The backtest accepts this data as legitimate, generating a fantastic result that is not reproducible live.

Fix: use only data from the previous candle (sdk.candles[-2]) to decide at the close of the current one. The current candle may be used for close (decision price).

Cause 2 - Optimistic execution model ​

Execution mode set to "optimistic": when stop and target are touched on the same candle, the target wins. Live, the stop wins; the mode is unrealistic.

Fix: in backtest settings, use pessimistic mode (default).

Cause 3 - probFillOnLimit = 1.0 on limit orders ​

With 100% fill on limit, every order that touches the price executes. In a real market, the order is not always ahead in the queue.

Fix: configure probFillOnLimit = 0.6 to simulate a realistic book.

Cause 4 - Zeroed fees ​

Without fees and slippage, marginally profitable strategies present unrealistic results.

Fix: verify the configuration section. Fees are on by default; if zeroed, restore them.

Cause 5 - Short backtest concentrated in a specific regime ​

Sharpe 3.0 over 3 months of bull market may be luck. There is no guarantee of survival across a full year with bear and sideways phases.

Fix: test across multiple periods (2021, 2022, 2023). Drastic metric degradation indicates overfitting.

"TimeoutError" ​

Execution exceeded 200ms.

Cause 1 - Heavy loop over all candles ​

python
# Incorrect: O(n^2), redoing it on every bar
for i in range(len(sdk.candles)):
    for j in range(len(sdk.candles)):
        ...

Fix: use only the current point or the last N bars. Do not iterate over the full history.

Cause 2 - pd.DataFrame(sdk.candles) on every bar ​

Building a DataFrame is costly. On every bar, over 10k backtest candles, the cumulative cost exceeds the timeout.

Fix: use a pure-Python list comprehension:

python
closes = [c["close"] for c in sdk.candles]

Cause 3 - Recursive indicators not cached ​

Recomputing EMA from scratch on every bar is O(n). Use an incremental version cached in sdk.state. See persistent state.

"SecurityError" ​

Code rejected by the engine before running.

Cause 1 - Forbidden import ​

python
import os   # SecurityError: Import not allowed: os

Fix: use only numpy, pandas, math, json, datetime, pandas_ta, talib. Details in sandbox limits.

Cause 2 - Blocked builtin ​

python
open("file.txt", "w")     # SecurityError: open is blocked
eval("1+1")               # SecurityError: eval is blocked

Fix: there is no I/O or dynamic execution. Rewrite the strategy without these calls.

Cause 3 - Dunder attribute ​

python
obj.__class__     # SecurityError: Forbidden attribute

Fix: avoid introspection. To check a type, use isinstance().

Cause 4 - Lambda ​

python
key = lambda x: x[1]   # SecurityError: Lambda is forbidden

Fix: use def:

python
def _key(x):
    return x[1]

"MemoryError" ​

Exceeded 64MB.

Cause - Accumulating lists without a limit ​

python
sdk.state["all_closes"] = sdk.state.get("all_closes", []) + [c["close"] for c in sdk.candles]
# Grows quadratically throughout the backtest

Fix: bound the size:

python
buf = sdk.state.setdefault("closes", [])
buf.append(sdk.candles[-1]["close"])
if len(buf) > 500:
    del buf[:len(buf) - 500]

"insufficient capital" ​

Order rejected because sdk.cash is less than the cost.

Cause 1 - qty=1 on crypto spot with a small balance ​

If sdk.cash = 100 USDT and BTC = 50000, qty=1 BTC costs 50000 and is rejected.

Fix: use size_pct or compute qty proportionally:

python
close = sdk.candles[-1]["close"]
qty = (sdk.cash * 0.25) / close  # 25% of cash
sdk.buy(action="buy_to_open", qty=qty, order_type="market")

Cause 2 - Very low initial balance ​

If the initial balance is BRL 1,000 and the asset is WIN (BRL 5,000/contract), the first trade is rejected.

Fix: increase the balance or use a compatible asset.

"ProtocolError: Strict Mode" ​

The script does not define a valid entrypoint.

Fix: it is mandatory to define main(df=None, sdk=None, params={}) or on_bar(sdk) at the root level.

Plot does not appear on the chart ​

The backtest runs, but the indicator line does not appear.

Cause 1 - Plot source different from the key in series ​

python
"plots": [{"name": "sma", "source": "sma_fast", ...}]   # source = "sma_fast"
"series": {"sma": [...]}                                  # key = "sma" - divergent

Fix: keep source equal to the key in series.

Cause 2 - Series array of different length than candles ​

The frontend discards a misaligned series.

Fix: ensure len(series["sma"]) == len(df). Use None for warmup.

Cause 3 - Missing the df= branch ​

python
def main(df=None, sdk=None, params={}):
    if sdk is not None:
        return on_bar_strategy(sdk, params)
    return DECLARATION
    # Missing: if df is not None: return _build_chart(df, params)

Without the df= branch, the frontend does not receive the series.

Next steps ​