My-Algo-Trading-Code

This contains all the code I have written for the signal generation and the front test where I fetch data using Dhan API

The code

Although I own the code, the coding itself was done entirely using GPT-5.4-xhigh, GPT-5.5-xhigh and Claude Opus 4.7, Claude Opus 4.8 on Max effort. GPT wrote majority of the signal generators and the data fetch files. Claude wrote the big one - the multithreaded Front Test worker. I just did the reviews and the testing

What is included?

• Data extractors which extract historical data for NIFTY/BANKNIFTY/FINNIFTY indices
• The backtest files I used to backtest
• The signal generators I created to generate signals
• The main front test file which uses miltithreading to execute all strategies together
• Live Telegram alerts: the front-test master file can post every entry/exit (option instrument, lot size, entry/exit price, and P&L) to a Telegram group/channel

Recent additions

• End-of-day P&L is now written to a Google Sheet. When all workers exit on a clean end of day, the master parses the run's log for each strategy's realised P&L and writes it into a tracker sheet — one row per strategy, one column per calendar day — overwriting today's cell and backfilling any blank earlier-this-month cells from the (append-mode) log. Auth is OAuth user-token via gspread; configure GSHEET_ID + an OAuth client in .env (see Setup). It's a safe no-op when unconfigured, so it never disturbs shutdown.
• 13 TradingBot signal-generator ports added — the front test now runs 24 workers. Thirteen more ATM single-leg strategies were ported into Signal Generators/ (SMA Crossover, Bollinger Bands, Keltner Squeeze, Mean Reversion Z-Score, ML Ensemble, Multi-Timeframe, Opening Range Breakout, Parabolic SAR, RSI Divergence, RSI Reversal, Stochastic, Supertrend, Volatility Breakout), all sharing misc_strategy_common.py (TA-Lib-first indicator helpers). They're wired into the master via one shared factory as AtmSingleLegStrategyWorkers — the same family as Renko/Goldmine/CPR — and each is fully tunable from .env by its own prefix (e.g. SMA_CROSSOVER_*, KELTNER_SQUEEZE_*). This brings the runner to 24 workers (21 ATM single-leg + 2 Hedged Puts + 1 Delta-0.2). ML Ensemble needs scikit-learn.
• CPR (Central Pivot Range) strategy is now live in the front test. It runs as an ATM single-leg worker (CPRStrategyWorker) alongside the other strategies: the master file feeds it 1-min OHLC, the CPR logic resamples to complete 5-min candles internally, and a LONG/SHORT signal buys the ATM CE/PE of the next-next expiry. Tunable via CPR_* knobs in the .env (lots, max-loss, poll, 09:25-15:15 window). (This brought the master file to nine workers at the time; see the latest addition at the top of this list for the current total.)
• Telegram trade notifications. A queue-based TelegramMessageWorker posts a message to a Telegram group/channel on every entry and exit from any worker. Each alert shows the strategy, the exact option instrument(s), lot size, entry and exit price, and P&L (hedged spreads show both legs). It runs on its own thread so Telegram latency or downtime never blocks the trading loop, and it's a cheap no-op when disabled. See Setup below to switch it on.

Pro Tip

You might have to adjust the import addresses from which the files are to be imported because the files are in different directories in my local machine(fixed in the latest Claude commit)

Repository structure

.
├── Nifty Multi Strategy Front Test - Master File.py   # multithreaded paper-trading runner
├── Data Extractors/                                   # 1m OHLC downloaders + shared helper
├── My Backtest Files (For Reference)/                 # backtesting.py-based backtests
└── Signal Generators/                                 # strategy / signal logic modules

Each subfolder has its own Readme.md with the details.

Setup

1. Python 3.10+ (I'm running 3.13).

2. Install dependencies:

   pip install dhanhq pandas numpy backtesting python-dotenv gspread
   

3. Set Dhan credentials as environment variables:

   DHAN_CLIENT_CODE=your_client_code
   DHAN_TOKEN_ID=your_access_token
   

   The backtests and the front-test master file additionally each load a local .env from their own folder for tunable strategy parameters.

4. (Optional) Turn on Telegram trade alerts by adding these to the master file's .env:

   TELEGRAM_ENABLED=true
   TELEGRAM_BOT_TOKEN=your_botfather_token
   TELEGRAM_CHAT_ID=@your_channel_or_-100xxxxxxxxxx
   

   Create the bot via @BotFather and add it to your group/channel as an admin. Leave TELEGRAM_ENABLED=false (the default) to run without alerts. The token stays in .env, which is git-ignored.

5. (Optional) End-of-day P&L to Google Sheets. After all workers exit, the master writes each strategy's day-end P&L into a tracker sheet (one row per strategy, one column per day, with month backfill). Enable it by adding to the master's .env:

   GSHEET_ID=your_spreadsheet_id
   GSHEET_OAUTH_CLIENT_FILE=Dependencies/gsheet_oauth_client.json
   GSHEET_OAUTH_TOKEN_FILE=Dependencies/gsheet_oauth_token.json
   

   Auth is OAuth user-token via gspread: in Google Cloud enable the Sheets API, create an OAuth client of type Desktop app, download its JSON to GSHEET_OAUTH_CLIENT_FILE, and share the sheet with your Google account. The first run opens a browser once for consent and caches a token at GSHEET_OAUTH_TOKEN_FILE. The sheet needs one row per strategy in column A with exact labels (e.g. Renko Strategy, SMA Crossover Strategy, Stochastic Oscillator Strategy, Supertrend Strategy, …); unmatched strategies are skipped with a warning. Leave GSHEET_ID blank to disable (safe no-op).

Typical workflow

1. Pull historical data — e.g. python "Data Extractors/Nifty 1m 5Y Data Fetch Dhan.py". The CSV lands in Backtest Outputs/.
2. Run a backtest against that CSV — e.g. python "My Backtest Files (For Reference)/Nifty Renko Strategy Backtest.py".
3. Once a strategy looks good, run Nifty Multi Strategy Front Test - Master File.py for paper-traded multi-strategy execution.

The Backtest Outputs/ folder is .gitignore-d, so generated CSVs/logs stay local.