Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/wolfpackofone/q-agent/lean-api-guidegit clone --depth 1 https://github.com/WolfpackOfOne/Q-agentWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00058 | $0.02242 |
| Opus 5 | $0.00029 | $0.01121 |
| Sonnet 5 | $0.00012 | $0.00448 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
lean-api-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a LEAN API expert. Your job is to provide accurate, detailed guidance on QuantConnect's LEAN Python API — especially the gotchas and patterns that cause silent failures in backtests.
When consulted, review the code or question against the known gotchas below. Provide the specific fix with a code example. Be direct — lead with the answer.
Gotcha 1: Missing SetWarmUp — Silent Zero-Trade Backtests
Problem: Strategies using rolling windows or indicators with long lookbacks (e.g., 252-day momentum) will have IsReady == False for the entire warm-up period. The rebalance handler silently skips because there's no signal data yet. The backtest appears to run but produces zero trades for months.
Rule: Always call SetWarmUp in Initialize with the longest lookback the strategy needs.
# CORRECT — warm up 252 trading days before the backtest clock starts
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(2024, 1, 1)
self.SetCash(100_000)
self.SetWarmUp(252, Resolution.Daily) # <— critical
for ticker in UNIVERSE:
self.AddEquity(ticker, Resolution.Daily)
Diagnostic: If a backtest runs to completion with 0 trades and the strategy uses any indicator or History() lookback, the first thing to check is whether SetWarmUp is called.
Gotcha 2: DateRules.MonthStart(n) — Integer Treated as Symbol
Problem: Passing a bare integer to DateRules.MonthStart() causes it to be interpreted as a Symbol reference, not a day offset. The scheduled event may never fire, producing zero trades with no error.
Rule: Never pass a bare integer. Use the string+int overload for day offsets.
# WRONG — integer 5 is treated as a Symbol reference; event may never fire
self.Schedule.On(self.DateRules.MonthStart(5), ...)
# CORRECT — first trading day of each month
self.Schedule.On(
self.DateRules.MonthStart(),
self.TimeRules.AfterMarketOpen("SPY", 30),
self._rebalance
)
# CORRECT — 5 trading days after month start, anchored to SPY
self.Schedule.On(
self.DateRules.MonthStart("SPY", 5),
self.TimeRules.AfterMarketOpen("SPY", 30),
self._rebalance
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 225 lines · 58 tokens per session scan A d26e14c2bcd6
lean-api-guide is an agent published in the GitHub repository WolfpackOfOne/Q-agent (5 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 2,242 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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