Python Algo Development, Backtesting and Research Tools
Custom Python code for strategy research: vectorised and event-driven backtests, walk-forward tests, market data pipelines and dashboards you can run and extend.
Python development
Research code that answers the question you actually asked
A TradingView backtest is a good first look. Serious questions, such as whether a strategy survives different regimes, costs and parameter choices, need code you control.
We build Python research tools with the discipline we teach in the Academy: next-bar execution, explicit costs, out-of-sample testing and results you can reproduce.
- Backtests in pandas or event-driven engines, with realistic costs
- Walk-forward and robustness tests instead of a single optimised result
- Data pipelines that clean, adjust and store market data reliably
- Pine to Python conversions for deeper testing of TradingView strategies
Software, not advice: we write code to the rules you specify. We do not provide investment advice, trading signals or managed accounts, and no backtest or script can guarantee future results.
Python · educational example
import pandas as pd
def backtest_sma(df: pd.DataFrame, fast=20, slow=50, cost_bps=5):
px = df["close"]
signal = (px.rolling(fast).mean() > px.rolling(slow).mean()).astype(int)
position = signal.shift(1).fillna(0) # act on the next bar: no look-ahead
returns = px.pct_change().fillna(0)
turnover = position.diff().abs().fillna(0)
net = position * returns - turnover * cost_bps / 10_000
equity = (1 + net).cumprod()
max_drawdown = (equity / equity.cummax() - 1).min()
return equity, max_drawdown # hypothetical results only
Python services
What we build in Python
Backtesting engines
Vectorised and event-driven backtests with costs, slippage and sizing.
Walk-forward testing
Rolling in-sample and out-of-sample tests that expose over-fitting.
Market data pipelines
Download, validate, adjust and store prices from the sources you license.
Research notebooks
Reproducible Jupyter notebooks that document every step of a study.
Dashboards
Internal dashboards for monitoring strategies, portfolios and data quality.
Portfolio analytics
Risk, exposure, correlation and drawdown reports across strategies.
API integrations
Connecting research code to the data and broker APIs you are licensed to use.
Engineering standards
How we keep Python research honest
No look-ahead
Signals act on the next bar and data is point-in-time wherever possible.
Explicit costs
Commission, spread and slippage are parameters, never hidden defaults.
Out-of-sample
Walk-forward and hold-out periods are reported separately from fitting.
Tested code
Unit tests, version control and a README so results can be reproduced.
Python development FAQs
Send a project briefQuestions about Python algo development
Scope, data, deployment and ownership.
Usually pandas, NumPy and established open-source backtesting and analysis libraries, or a lightweight custom engine when the strategy needs one. We agree the stack in the specification.
No. We work with data you are licensed to use, from your broker, a data vendor or exchange files, and we build tools to clean and validate it.
We can discuss integrations with broker APIs you are licensed to use. You remain responsible for live trading decisions and for complying with your broker's and regulator's rules, including SEBI's framework for algorithmic trading in India.
Yes, remotely, as well as clients in the UK, Europe, Canada, Australia, Singapore and the UAE.
Yes. You receive the full, commented source code. Ownership, licensing and confidentiality terms are agreed in writing before work starts.
Each project is quoted after we review your brief and agree a written specification. Price depends on the number of rules, timeframes, markets, alerts and revisions involved. We do not publish a fixed price list yet.
No, and you should be wary of anyone who says they can. We implement and test the rules you give us and report the results honestly, including costs, slippage and drawdowns. Backtests are hypothetical and do not predict future performance.
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Need a backtest or research tool built in Python?
Tell us the strategy, the data you have and the questions you want answered. We reply with a written specification and quote.