[WIP] Basic Fib Implementation

Backetester.py

@@ -4,6 +4,7 @@
 import RSIBacktester
 import SMABacktester
 import SOBacktester
+import FibonacciBacktester
 
 class Backtester:
     ''' Class for the vectorized backtesting of trading strategies.
@@ -92,6 +93,7 @@ def strategy_setup(self, strategy):
             "RSI": lambda: RSIBacktester.RSIBacktester(self.symbol, self.options["RSI_periods"], self.options["RSI_upper"], self.options["RSI_lower"], self.start, self.end, self.granularity, self.options["TC"]),
             "SMA": lambda: SMABacktester.SMABacktester(self.symbol, self.options["SMA_S"], self.options["SMA_L"], self.start, self.end, self.granularity, self.options["TC"]),
             "SO": lambda: SOBacktester.SOBacktester(self.symbol, self.options["SO_periods"], self.options["SO_D_mw"], self.start, self.end, self.granularity, self.options["TC"]),
+            "FIB": lambda: FibonacciBacktester.FibonacciBacktester(self.symbol, self.start, self.end, self.granularity),
         }
         self.data = switch[strategy]().data
 

FibonacciBacktester.py

@@ -0,0 +1,99 @@
+import os
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from oandapyV20 import API
+from dotenv import load_dotenv
+import oandapyV20.endpoints.instruments as instruments
+
+load_dotenv()
+oanda_api_key = os.getenv('OANDA_API_KEY')
+
+class FibonacciBacktester():
+    def __init__(self, symbol, start, end, granularity):
+        self.symbol = symbol
+        self.start = start
+        self.end = end
+        self.granularity = granularity
+        self.data = None
+
+    def __repr__(self):
+        return "FibonacciBacktester(symbol={}, start={}, end={}, granularity={})".format(self.symbol, self.start, self.end, self.granularity)
+
+    def get_data(self):
+        client = API(access_token=oanda_api_key)
+
+        params = {
+            "from": self.start,
+            "to": self.end,
+            "granularity": self.granularity,
+        }
+
+        request = instruments.InstrumentsCandles(instrument=self.symbol, params=params)
+        client.request(request)
+        response = request.response
+
+        candles = response['candles']
+        data_list = []
+
+        for candle in candles:
+            time = pd.to_datetime(candle['time'])
+            close_price = float(candle['mid']['c'])
+
+            data_list.append([time, close_price])
+
+        columns = ['Date', 'Close']
+        data = pd.DataFrame(data_list, columns=columns)
+        data.set_index('Date', inplace=True)
+
+        data["returns"] = np.log(data["Close"] / data["Close"].shift(1))
+        self.data = data
+
+    def find_local_highs_lows(self):
+        if self.data is None:
+            print("No data available for finding local highs and lows. Use get_data() to fetch data.")
+            return
+
+        data = self.data.copy()
+        data["local_high"] = False
+        data["local_low"] = False
+
+        for i in range(1, len(data) - 1):
+            if data["Close"][i] > data["Close"][i - 1] and data["Close"][i] > data["Close"][i + 1]:
+                data.at[data.index[i], "local_high"] = True
+            elif data["Close"][i] < data["Close"][i - 1] and data["Close"][i] < data["Close"][i + 1]:
+                data.at[data.index[i], "local_low"] = True
+
+        self.data = data
+
+    def plot_results(self):
+        if self.data is None:
+            print("No data available for plotting. Use get_data() to fetch data.")
+            return
+
+        fig, ax = plt.subplots(figsize=(12, 6))
+        ax.plot(self.data.index, self.data['Close'], label='Price')
+
+        local_highs = self.data[self.data['local_high']]
+        local_lows = self.data[self.data['local_low']]
+        ax.scatter(local_highs.index, local_highs['Close'], marker='^', color='red', label='Local High')
+        ax.scatter(local_lows.index, local_lows['Close'], marker='v', color='green', label='Local Low')
+
+        ax.set_title(f"Local Highs and Lows for {self.symbol}")
+        ax.set_xlabel("Date")
+        ax.set_ylabel("Price")
+
+        ax.legend()
+
+        # Manually calculate and plot Fibonacci retracement levels
+        price_data = self.data['Close'].to_numpy()
+        data_length = len(price_data)
+        fib_levels = {}
+        for level in [0.236, 0.382, 0.618]:
+            index = int(data_length * level)
+            fib_levels[level] = price_data.max() - (price_data.max() - price_data.min()) * level
+            ax.axhline(fib_levels[level], color='blue', linestyle='--', label=f'Fib {level}')
+
+        plt.show()
+
+