@backtest-kit/ollama

🤖 @backtest-kit/ollama

Multi-provider LLM context wrapper for trading strategies. Supports 10+ providers with unified HOF API.

Transform technical analysis into trading decisions with multi-provider LLM support, structured output, and built-in risk management.

📚 Backtest Kit Docs | 🌟 GitHub

New to backtest-kit? The fastest way to get a real, production-ready setup is to clone the reference implementation — a fully working news-sentiment AI trading system with LLM forecasting, multi-timeframe data, and a documented February 2026 backtest. Start there instead of from scratch.

✨ Features

• 🔌 10+ LLM Providers: OpenAI, Claude, DeepSeek, Grok, Mistral, Perplexity, Cohere, Alibaba, Hugging Face, Ollama, GLM-4
• ⚡ Higher-Order Functions: Wrap async functions with inference context via di-scoped
• 📝 Userspace Prompts: Load prompts from .cjs modules in config/prompt/
• 🎯 Userspace Schema: Define your own Zod or JSON schema with addOutline
• 🔄 Token Rotation: Pass array of API keys for automatic rotation
• 🗄️ Memoized Cache: Prompt modules cached with memoize from functools-kit

📦 Installation

npm install @backtest-kit/ollama backtest-kit agent-swarm-kit

🚀 Usage

Signal Schema (userspace)

// schema/Signal.schema.ts
import { z } from 'zod';
import { str } from 'functools-kit';

export const SignalSchema = z.object({
  position: z.enum(['long', 'short', 'wait']).describe(
    str.newline(
      'Position direction:',
      'long: bullish signals, uptrend potential',
      'short: bearish signals, downtrend potential',
      'wait: conflicting signals or unfavorable conditions',
    )
  ),
  price_open: z.number().describe(
    str.newline(
      'Entry price in USD',
      'Current market price or limit order price',
    )
  ),
  price_stop_loss: z.number().describe(
    str.newline(
      'Stop-loss price in USD',
      'LONG: below price_open',
      'SHORT: above price_open',
    )
  ),
  price_take_profit: z.number().describe(
    str.newline(
      'Take-profit price in USD',
      'LONG: above price_open',
      'SHORT: below price_open',
    )
  ),
  minute_estimated_time: z.number().describe(
    'Estimated time to reach TP in minutes'
  ),
  risk_note: z.string().describe(
    str.newline(
      'Risk assessment:',
      '- Whale manipulations',
      '- Order book imbalance',
      '- Technical divergences',
      'Provide specific numbers and percentages',
    )
  ),
});

export type TSignalSchema = z.infer<typeof SignalSchema>;

Signal Outline with Zod (userspace)

// outline/signal.outline.ts
import { addOutline } from 'agent-swarm-kit';
import { zodResponseFormat } from 'openai/helpers/zod';
import { SignalSchema, TSignalSchema } from '../schema/Signal.schema';
import { CompletionName } from '@backtest-kit/ollama';

addOutline<TSignalSchema>({
  outlineName: 'SignalOutline',
  completion: CompletionName.RunnerOutlineCompletion,
  format: zodResponseFormat(SignalSchema, 'position_decision'),
  getOutlineHistory: async ({ history, param: messages = [] }) => {
    await history.push(messages);
  },
  validations: [
    {
      validate: ({ data }) => {
        if (data.position === 'long' && data.price_stop_loss >= data.price_open) {
          throw new Error('For LONG, stop_loss must be below price_open');
        }
        if (data.position === 'short' && data.price_stop_loss <= data.price_open) {
          throw new Error('For SHORT, stop_loss must be above price_open');
        }
      },
    },
  ],
});

Signal Outline without Zod (userspace)

// outline/signal.outline.ts
import { addOutline, IOutlineFormat } from 'agent-swarm-kit';
import { CompletionName } from '@backtest-kit/ollama';

const format: IOutlineFormat = {
  type: 'object',
  properties: {
    take_profit_price: { type: 'number', description: 'Take profit price in USD' },
    stop_loss_price: { type: 'number', description: 'Stop-loss price in USD' },
    description: { type: 'string', description: 'User-friendly explanation of risks, min 10 sentences' },
    reasoning: { type: 'string', description: 'Technical analysis, min 15 sentences' },
  },
  required: ['take_profit_price', 'stop_loss_price', 'description', 'reasoning'],
};

addOutline({
  outlineName: 'SignalOutline',
  format,
  prompt: 'Generate crypto trading signals based on price and volume indicators in JSON format.',
  completion: CompletionName.RunnerOutlineCompletion,
  getOutlineHistory: async ({ history, param }) => {
    const signalReport = await ioc.signalReportService.getSignalReport(param);
    await commitReports(history, signalReport);
    await history.push({ role: 'user', content: 'Generate JSON based on reports.' });
  },
  validations: [
    {
      validate: ({ data }) => {
        if (data.action !== 'buy') return;
        const stopLossChange = percentDiff(data.current_price, data.stop_loss_price);
        if (stopLossChange > CC_LADDER_STOP_LOSS) {
          throw new Error(`Stop loss must not exceed -${CC_LADDER_STOP_LOSS}%`);
        }
      },
      docDescription: 'Checks stop-loss price against max loss percentage.',
    },
    {
      validate: ({ data }) => {
        if (data.action !== 'buy') return;
        const sellChange = percentDiff(data.current_price, data.take_profit_price);
        if (sellChange > CC_LADDER_TAKE_PROFIT) {
          throw new Error(`Take profit must not exceed +${CC_LADDER_TAKE_PROFIT}%`);
        }
      },
      docDescription: 'Checks take-profit price against max profit percentage.',
    },
  ],
});

Prompt Module (userspace)

// config/prompt/signal.prompt.cjs
module.exports = {
  system: (symbol, strategyName, exchangeName, frameName, backtest) => [
    `You are analyzing ${symbol} on ${exchangeName}`,
    `Strategy: ${strategyName}, Timeframe: ${frameName}`,
    backtest ? 'Backtest mode' : 'Live mode',
  ],
  user: (symbol) => `Analyze ${symbol} and return trading decision`,
};

Strategy

// strategy.ts
import './outline/signal.outline'; // register outline

import { deepseek, Module, commitPrompt, MessageModel } from '@backtest-kit/ollama';
import { addStrategy } from 'backtest-kit';
import { json } from 'agent-swarm-kit';

const signalModule = Module.fromPath('./signal.prompt.cjs');

const getSignal = async () => {
  const messages: MessageModel[] = [];
  await commitPrompt(signalModule, messages);

  const { data } = await json('SignalOutline', messages);
  return data;
};

addStrategy({
  strategyName: 'llm-signal',
  interval: '5m',
  getSignal: deepseek(getSignal, 'deepseek-chat', process.env.DEEPSEEK_API_KEY),
});

Dynamic Prompt

// config/prompt/risk.prompt.cjs
module.exports = {
  system: ['You are a risk analyst', 'Be conservative'],
  user: (symbol, strategyName, exchangeName, frameName, backtest) =>
    `Evaluate risk for ${symbol} position on ${frameName} timeframe`,
};

Inline Prompt

import { Prompt, commitPrompt, MessageModel } from '@backtest-kit/ollama';

const prompt = Prompt.fromPrompt({
  system: ['You are a trading bot'],
  user: (symbol) => `What is the trend for ${symbol}?`,
});

const messages: MessageModel[] = [];
await commitPrompt(prompt, messages);

Token Rotation

import { ollama } from '@backtest-kit/ollama';

const wrappedFn = ollama(myFn, 'llama3.3:70b', ['key1', 'key2', 'key3']);

🔌 Providers

Provider	Function	Base URL
OpenAI	gpt5()	https://api.openai.com/v1/
Claude	claude()	https://api.anthropic.com/v1/
DeepSeek	deepseek()	https://api.deepseek.com/
Grok	grok()	https://api.x.ai/v1/
Mistral	mistral()	https://api.mistral.ai/v1/
Perplexity	perplexity()	https://api.perplexity.ai/
Cohere	cohere()	https://api.cohere.ai/compatibility/v1/
Alibaba	alibaba()	https://dashscope-intl.aliyuncs.com/compatible-mode/v1/
Hugging Face	hf()	https://router.huggingface.co/v1/
Ollama	ollama()	http://localhost:11434/
Zhipu AI	glm4()	https://open.bigmodel.cn/api/paas/v4/

📖 API

Provider HOF

ollama | gpt5 | claude | deepseek | grok | mistral | perplexity | cohere | alibaba | hf | glm4
(fn, model, apiKey?) => fn

Module

Module.fromPath(path: string, baseDir?: string): Module

Default baseDir: {cwd}/config/prompt/

Prompt

Prompt.fromPrompt(source: PromptModel): Prompt

commitPrompt

async function commitPrompt(source: Module | Prompt, history: MessageModel[]): Promise<void>

PromptModel

interface PromptModel {
  system?: string[] | SystemPromptFn;
  user: string | UserPromptFn;
}

type SystemPromptFn = (
  symbol: string,
  strategyName: string,
  exchangeName: string,
  frameName: string,
  backtest: boolean
) => Promise<string[]> | string[];

type UserPromptFn = (
  symbol: string,
  strategyName: string,
  exchangeName: string,
  frameName: string,
  backtest: boolean
) => Promise<string> | string;

💡 Why Use @backtest-kit/ollama?

Instead of manually integrating LLM SDKs:

❌ Without ollama (manual work)

import OpenAI from 'openai';
import Anthropic from '@anthropic-ai/sdk';

const openai = new OpenAI({ apiKey: process.env.OPENAI_KEY });
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages,
  response_format: { type: 'json_object' }
});
const signal = JSON.parse(response.choices[0].message.content);
// ... manual schema validation
// ... manual error handling
// ... no fallback

✅ With ollama (one line)

const signal = await gpt5(messages, 'gpt-4o');

🔥 Benefits:

• ⚡ Unified API across 10+ providers
• 🎯 Enforced JSON schema (no parsing errors)
• 🔄 Built-in token rotation (Ollama)
• 🔑 Context-based API keys
• 🛡️ Type-safe TypeScript interfaces
• 📊 Trading-specific output format

🤝 Contribute

Fork/PR on GitHub.

📜 License

MIT © tripolskypetr