Algorithmic Stock Strategies for Backtesting

I already run my own data feeds, execution simulator and paper-trading environment, so the missing piece is a set of fresh, well-defined algorithmic strategies focused solely on equities. I’m open to any blend of trend-following, market-making or statistical-arbitrage logic—what matters is that the rules are explicit enough for me to drop straight into my existing Python back-tester.

Here’s what I need from you:
• A clear narrative of each strategy’s concept and edge.
• Precise entry, exit, position-sizing and risk parameters.
• Any indicator formulas or data transformations required.
• Clean, well-commented Python (or pseudo-code I can quickly port) that compiles without external editing.
• Brief notes on walk-forward or parameter-optimisation suggestions so I can validate results beyond the initial back-test.

I’ll run each submission through my engine and paper-trade them for at least two weeks; strategies that match their stated behaviour and stay within drawdown limits will be considered complete.

If you’ve previously deployed or researched stock algorithms and can package them in a plug-and-play format, let’s talk.

Thank you for the questions. Below is a consolidated clarification to help you assess the project and prepare your proposal. Detailed technical specifications and proprietary requirements will be shared with the selected freelancer under an appropriate confidentiality agreement.

1. Objective and scope

I am looking for an experienced quantitative researcher/developer to propose and implement systematic equity strategies for integration into an existing Python backtesting environment.

The intended progression is independent backtesting, portfolio evaluation, paper trading and potentially live deployment. Progression depends on satisfactory validation; delivery of a strategy does not automatically qualify it for trading.

The immediate scope is strategy research, implementation and reproducible testing. Any substantial production infrastructure or live brokerage integration should be quoted separately.

2. Markets and strategy proposals

The initial focus is US-listed equities. This is not a sports-betting, cryptocurrency, forex or options project.

I welcome proposals from different strategy families. Please recommend the approach you believe you can substantiate best, explain its economic rationale and identify the conditions in which it should work or fail.

Daily-data strategies are the preferred starting point. State the expected holding period and trading frequency. Intraday or market-making proposals must clearly identify their additional data and execution requirements.

Please assume long-only for the initial proposal. Any dependence on short selling, leverage or specialised order handling must be disclosed rather than assumed.

3. Data and universe

Historical daily price and volume data are available. Additional fundamental or event data may be considered where required and where coverage and licensing permit.

The selected freelancer will receive the relevant data specification, sample format, confirmed historical coverage and integration requirements. Python/pandas-compatible inputs are expected; the precise exchange format will be agreed before implementation.

Please specify:

- Required fields and minimum historical coverage.
- Proposed universe and liquidity requirements.
- Any external datasets, subscriptions or recurring costs.
- How you handle missing data, delisted securities, corporate actions and historical changes in the universe.

Exact dataset size and usable date ranges depend on the selected strategy and will be confirmed during technical scoping. Please do not assume that tick data, order-book data or historical bid/ask quotes are available.

4. Strategy interface and performance requirements

An existing Python research and backtesting environment is in place. The selected freelancer will receive the relevant interface specification rather than being expected to rebuild the platform.

The implementation should be modular, with clearly defined inputs and outputs. Strategy logic should remain separate from brokerage connectivity and portfolio-level controls.

The immediate requirement is reliable batch backtesting and repeatable signal generation. There is no high-frequency latency target for this phase. Please state expected runtime, memory requirements and any scalability limitations.

5. Risk management and trading assumptions

Existing portfolio-level risk controls must be respected. The freelancer should also define the strategy’s entry, exit, sizing, exposure and failure conditions.

Exact capital assumptions, position limits, drawdown thresholds and internal acceptance criteria are confidential and will be provided to the selected freelancer before implementation and testing.

Backtests must include realistic transaction costs and execution assumptions, including spreads, slippage and other applicable charges. Higher-cost scenarios should also be evaluated. Order types, fill assumptions and restrictions will be agreed during scoping.

Risk requirements must remain fixed during optimization and cannot be relaxed to improve reported results.

6. Validation and optimization

I am looking for reproducible evidence rather than an attractive historical equity curve alone.

The work should address:

- Separation of development and out-of-sample evaluation.
- Walk-forward testing where appropriate.
- Look-ahead, survivorship and selection bias.
- Parameter sensitivity and the risk of overfitting.
- Realistic costs, execution delays and liquidity constraints.
- Performance across different market conditions.
- Suitable benchmark comparisons.
- Disclosure of the variants tested, including unsuccessful results.

Please explain your proposed validation method and how you prevent repeated optimization from contaminating the test results. Our detailed internal evaluation framework will be shared privately with the selected freelancer.

7. Questrade compatibility

The strategy should be designed so its outputs can later be connected to Questrade through an appropriate integration layer, including Questrade MCP where supported.

Please keep the strategy implementation independent of the broker and identify any assumptions about supported instruments, order types, market data or approval requirements.

Questrade MCP currently requires user approval for trade instructions, so a proposal should not depend on instantaneous, unattended execution through that connection.

For this phase, please describe the integration approach and any limitations. Production connectivity and live execution will require a separate agreed scope.

8. Deliverables and reporting

Expected deliverables include:

- A clear explanation of the strategy and its rationale.
- Precise rules, formulas, parameters and data requirements.
- Runnable, documented Python code with setup instructions and dependencies.
- Sample inputs and outputs, plus relevant implementation tests.
- Reproducible backtest results and a trade-level record.
- A concise report covering performance, drawdowns, costs, exposure, robustness and limitations.

Charts should support the analysis, including equity and drawdown curves and relevant comparisons. A separate dashboard is not required unless agreed.

Please identify any third-party code, licensing restrictions or ongoing service dependencies.

9. Budget, milestones and acceptance

Within the advertised budget, please recommend a realistic scope. My preference is to assess a small shortlist of concepts and implement one selected strategy thoroughly as the first milestone. Further strategies can be agreed separately.

Please distinguish contract deliverables from investment performance. Acceptance criteria will be agreed before work starts, and no guarantee of profitability is expected.

The original reference to two weeks of paper trading should be understood as an initial operational check. The full evaluation period will depend on trading frequency and the amount of evidence required. Live deployment remains a separate decision.

In your proposal, please include your relevant equity-strategy experience, recommended approach, validation methodology, data requirements, delivery timeline, milestone pricing and key limitations. Non-confidential examples of previous work are welcome.